Novel plant disease detection techniques-a brief review

Plant pathogens cause severe losses to agricultural yield worldwide. Tracking plant health and early disease detection is important to reduce the disease spread and thus economic loss. Though visual scouting has been practiced from former times, detection of asymptomatic disease conditions is difficult. So, DNA-based and serological methods gained importance in plant disease detection. The progress in advanced technologies challenges the development of rapid, non-invasive, and on-field detection techniques such as spectroscopy. This review highlights various direct and indirect ways of detecting plant diseases like Enzyme-linked immunosorbent assay, Lateral flow assays, Polymerase chain reaction, spectroscopic techniques and biosensors. Although these techniques are sensitive and pathogen-specific, they are more laborious and time-intensive. As a consequence, a lot of interest is gained in in-field adaptable point-of-care devices with artificial intelligence-assisted pathogen detection at an early stage. More recently computer-aided techniques like neural networks are gaining significance in plant disease detection by image processing. In addition, a concise report on the latest progress achieved in plant disease detection techniques is provided.

Skin pore imaging using spectral-domain optical coherence tomography: a case report

Sebum is an important component of the skin that has attracted attention in many fields, including dermatology and cosmetics. Pore expansion due to sebum on the skin can lead to various problems. Therefore, it is necessary to analyze the morphological characteristics of sebum. In this study, we used optical coherence tomography (OCT) to evaluate facial sebum areas. We obtained the OCT maximum amplitude projection (MAP) image and a cross-sectional image of skin pores in the facial area. Subsequently, we detected the sebum in skin pores using the detection algorithm of the ImageJ software to quantitatively determine the size of randomly selected pores in the proposed MAP images. Additionally, the pore size was analyzed by acquiring images before and after facial sebum extraction. According to our research, facial sebum can be morphologically described using the OCT system. Since OCT imaging enables specific analysis of skin parameters, including pores and sebum, skin analysis employing OCT could be an effective method for further research.

Exploring the role of texture features in deep convolutional neural networks: Insights from Portilla-Simoncelli statistics

It is well-understood that the performance of Deep Convolutional Neural Networks (DCNNs) in image recognition tasks is influenced not only by shape but also by texture information. Despite this, understanding the internal representations of DCNNs remains a challenging task. This study employs a simplified version of the Portilla-Simoncelli Statistics, termed "minPS," to explore how texture information is represented in a pre-trained VGG network. Using minPS features extracted from texture images, we perform a sparse regression on the activations across various channels in VGG layers. Our findings reveal that channels in the early to middle layers of the VGG network can be effectively described by minPS features. Additionally, we observe that the explanatory power of minPS sub-groups evolves as one ascends the network hierarchy. Specifically, sub-groups termed Linear Cross Scale (LCS) and Energy Cross Scale (ECS) exhibit weak explanatory power for VGG channels. To investigate the relationship further, we compare the original texture images with their synthesized counterparts, generated using VGG, in terms of minPS features. Our results indicate that the absence of certain minPS features suggests their non-utilization in VGG's internal representations.

A comparative study of Machine Learning-based classification of Tomato fungal diseases: Application of GLCM texture features

Globally, agriculture remains an important source of food and economic development. Due to various plant diseases, farmers continue to suffer huge yield losses in both quality and quantity. In this study, we explored the potential of using Artificial Neural Networks, K-Nearest Neighbors, Random Forest, and Support Vector Machine to classify tomato fungal leaf diseases: Alternaria, Curvularia, Helminthosporium, and Lasiodiplodi based on Gray Level Co-occurrence Matrix texture features. Small differences between symptoms of these diseases make it difficult to use the naked eye to obtain better results in detecting and distinguishing these diseases. The Artificial Neural Network outperformed other classifiers with an overall accuracy of 94% and average scores of 93.6% for Precision, 93.8% for Recall, and 93.8% for F1-score. Generally, the models confused samples originally belonging to Helminthosporium with Curvularia. The extracted texture features show great potential to classify the different tomato leaf fungal diseases. The results of this study show that texture characteristics of the Gray Level Co-occurrence Matrix play a critical role in the establishment of tomato leaf disease classification systems and can facilitate the implementation of preventive measures by farmers, resulting in enhanced yield quality and quantity.

Artificial Intelligence Optical Biopsy for Evaluating the Functional State of Wounds

INTRODUCTION

The clinical characterization of the functional status of active wounds in terms of their driving cellular and molecular biology remains a considerable challenge that currently requires excision via a tissue biopsy. In this pilot study, we use convolutional Siamese neural network (SNN) architecture to predict the functional state of a wound using digital photographs of wounds in a canine model of volumetric muscle loss (VML).

METHODS

Digital images of VML injuries and tissue biopsies were obtained in a standardized fashion from an established canine model of VML. Gene expression profiles for each biopsy site were obtained using RNA sequencing. These profiles were converted to functional profiles by a manual review of validated gene ontology databases in which we determined a hierarchical representation of gene functions based on functional specificity. An SNN was trained to regress functional profile expression values, informed by an image segment showing the surface of a small tissue biopsy.

RESULTS

The SNN was able to predict the functional expression of a range of functions based with error ranging from ∼5% to ∼30%, with functions that are most closely associated with the early state of wound healing to be those best-predicted.

CONCLUSIONS

These initial results suggest promise for further research regarding this novel use of machine learning regression on medical images. The regression of functional profiles, as opposed to specific genes, both addresses the challenge of genetic redundancy and gives a deeper insight into the mechanistic configuration of a region of tissue in wounds.

Advancements in oral and maxillofacial surgery medical images segmentation techniques: An overview

OBJECTIVES

This article reviews recent advances in computer-aided segmentation methods for oral and maxillofacial surgery and describes the advantages and limitations of these methods. The objective is to provide an invaluable resource for precise therapy and surgical planning in oral and maxillofacial surgery. Study selection, data and sources: This review includes full-text articles and conference proceedings reporting the application of segmentation methods in the field of oral and maxillofacial surgery. The research focuses on three aspects: tooth detection segmentation, mandibular canal segmentation and alveolar bone segmentation. The most commonly used imaging technique is CBCT, followed by conventional CT and Orthopantomography. A systematic electronic database search was performed up to July 2023 (Medline via PubMed, IEEE Xplore, ArXiv, Google Scholar were searched).

RESULTS

These segmentation methods can be mainly divided into two categories: traditional image processing and machine learning (including deep learning). Performance testing on a dataset of images labeled by medical professionals shows that it performs similarly to dentists' annotations, confirming its effectiveness. However, no studies have evaluated its practical application value.

CONCLUSION

Segmentation methods (particularly deep learning methods) have demonstrated unprecedented performance, while inherent challenges remain, including the scarcity and inconsistency of datasets, visible artifacts in images, unbalanced data distribution, and the "black box" nature.

CLINICAL SIGNIFICANCE

Accurate image segmentation is critical for precise treatment and surgical planning in oral and maxillofacial surgery. This review aims to facilitate more accurate and effective surgical treatment planning among dental researchers.

An experimental setup and segmentation method for CFU counting on agar plate for the assessment of drinking water

Quantification of bacterial colonies on an agar plate is a daily routine for a microbiologist to determine the number of viable microorganisms in the sample. In general, microbiologists perform a visual assessment of bacterial colonies which is time-consuming (takes 2 min per plate), tedious, and subjective. Some automatic counting algorithms are developed that save labour and time, but their results are affected by the non-illumination on an agar plate. To improve this, the present manuscript aims to develop an inexpensive and efficient device to acquire S.aureus images via an automatic counting method using image processing techniques under real laboratory conditions. The proposed method (P_ColonyCount) includes the region of interest extraction and color space transformation followed by filtering, thresholding, morphological operation, distance transform, and watershed technique for the quantification of isolated and overlapping colonies. The present work also shows a comparative study on grayscale, K, and green channels by applying different filter and thresholding techniques on 42 images. The results of all channels were compared with the score provided by the expert (manual count). Out of all the proposed method (P_ColonyCount), the K channel gives the best outcome in comparison with the other two channels (grayscale and green) in terms of precision, recall, and F-measure which are 0.99, 0.99, and 0.99 (2 h), 0.98, 0.99, and 0.98 (4 h), and 0.98, 0.98, 0.98 (6 h) respectively. The execution time of the manual and the proposed method (P_ColonyCount) for 42 images ranges from 19 to 113 s and 15 to 31 s respectively. Apart from this, a user-friendly graphical user interface is also developed for the convenient enumeration of colonies without any expert knowledge/training. The developed imaging device will be useful for researchers and teaching lab settings.

Simulation-aided infrared thermography with decomposition-based noise reduction for detecting defects in ancient polyptychs

In recent years, the conservation and protection of ancient cultural heritage have received increasing attention, and non-destructive testing (NDT), which can minimize the damage done to the test subject, plays an integral role therein. For instance, NDT through active infrared thermal imaging can be applied to ancient polyptychs, which can realize accurate detection of damage and defects existing on the surface and interior of the polyptychs. In this study, infrared thermography is used for non-invasive investigation and evaluation of two polyptych samples with different pigments and artificial defects, but both reproduced based on a painting by Pietro Lorenzetti (1280/85-1348) using the typical tempera technique of the century. It is noted that, to avoid as far as possible secondary damages done to the ancient cultural heritages, repeated damage-detection experiments are rarely carried out on the test subjects. To that end, numerical simulation is used to reveal the heat transfer properties and temperature distributions, as to perform procedural verification and reduce the number of experiments that need to be conducted on actual samples. Technique-wise, to improve the observability of the experimental results, a total variation regularized low-rank tensor decomposition algorithm is implemented to reduce the background noise and improve the contrast of the images. Furthermore, the efficacy of image processing is quantified through the structural-similarity evaluation.

Identification and estimation of lodging in bread wheat genotypes using machine learning predictive algorithms

BACKGROUND

Lodging or stem bending decreases wheat yield quality and quantity. Thus, the traits reflected in early lodging wheat are helpful for early monitoring to some extent. In order to identify the superior genotypes and compare multiple linear regression (MLR) with support vector regression (SVR), artificial neural network (ANN), and random forest regression (RF) for predicting lodging in Iranian wheat accessions, a total of 228 wheat accessions were cultivated under field conditions in an alpha-lattice experiment, randomized incomplete block design, with two replications in two cropping seasons (2018-2019 and 2019-2020). To measure traits, a total of 20 plants were isolated from each plot and were measured using image processing.

RESULTS

The lodging score index (LS) had the highest positive correlation with plant height (r = 0.78**), Number of nodes (r = 0.71**), and internode length 1 (r = 0.70**). Genotypes were classified into four groups based on heat map output. The most lodging-resistant genotypes showed a lodging index of zero or close to zero. The findings revealed that the RF algorithm provided a more accurate estimate (R2 = 0.887 and RMSE = 0.091 for training data and R2 = 0.768 and RMSE = 0.124 for testing data) of wheat lodging than the ANN and SVR algorithms, and its robustness was as good as ANN but better than SVR.

CONCLUSION

Overall, it seems that the RF model can provide a helpful predictive and exploratory tool to estimate wheat lodging in the field. This work can contribute to the adoption of managerial approaches for precise and non-destructive monitoring of lodging.

On the utility of Colour in shape analysis: An introduction to Colour science via palaeographical case studies

In this article, we explore the use of colour for the analysis of shapes in digital images. We argue that colour can provide unique information that is not available from shape alone, and that familiarity with the interdisciplinary field of colour science is essential for unlocking the potential of colour. Within this perspective, we offer an illustrated overview of the colour-related aspects of image management and processing, perceptual psychology, and cultural studies, using for exemplary purposes case studies focused on computational palaeography. We also discuss the changing roles of colour in society and the sciences, and provide technical solutions for using digital colour effectively, highlighting the impact of human factors. The article concludes with an annotated bibliography. This work is a primer, and its intended readership are scholars and computer scientists unfamiliar with colour science.

Widefield oct-angiography-based classification of sickle cell retinopathy

PURPOSE

To assess the capillary non-perfusion in different concentric sectors on widefield optical coherence tomography angiography (WF-OCTA) and to correlate the ratio of non-perfusion (RNP) to the severity of sickle cell retinopathy (SCR).

METHODS

This retrospective, cross-sectional study included eyes of patients with various sickle cell disease (SCD) genotypes having undergone WF-OCTA and ultra-widefield color fundus photography (UWF-CFP). Eyes were grouped as no SCR, non-proliferative SCR or proliferative SCR. RNP was assessed on WF-OCTA montage in different field-of-view (FOV) sectors centered on the fovea: 0-10-degrees circle excluding the foveal avascular zone, the 10-30-degrees circle excluding the optic nerve, the 30-60-degrees circle, and the full 60-degrees circle.

RESULTS

Forty-two eyes of twenty-eight patients were included. Within each SCR group, mean RNP of the FOV 30-60 sector was higher than all other sectors (p < 0.05). Mean RNP of all sectors were significatively different between no SCR group and proliferative SCR group (p < 0.05). To distinguish no SCR versus non-proliferative SCR FOV 30-60 had a good sensitivity and specificity of 41.67% and 93.33%, respectively (cutoff RNP > 22.72%, AUC = 0.75, 95% CI 0.56-0.94, p = 0.028). To differentiate non-proliferative versus proliferative SCR, FOV 0-10 had good sensitivity and specificity of 33.33% and 91.67%, respectively (cutoff RNP > 18.09, AUC = 0.73, 95% CI 0.53 to 0.93, p = 0.041). To discern no SCR versus proliferative SCR, all sectors had optimal sensitivity and specificity (p < 0.05).

CONCLUSION

WF OCTA-based RNP provides non-invasive diagnostic information regarding the presence and severity of SCR, and correlates with disease stage in certain FOV sectors.

Artificial Intelligence Techniques for Automatic Detection of Peri-implant Marginal Bone Remodeling in Intraoral Radiographs

Peri-implantitis can cause marginal bone remodeling around implants. The aim is to develop an automatic image processing approach based on two artificial intelligence (AI) techniques in intraoral (periapical and bitewing) radiographs to assist dentists in determining bone loss. The first is a deep learning (DL) object-detector (YOLOv3) to roughly identify (no exact localization is required) two objects: prosthesis (crown) and implant (screw). The second is an image understanding-based (IU) process to fine-tune lines on screw edges and to identify significant points (intensity bone changes, intersections between screw and crown). Distances between these points are used to compute bone loss. A total of 2920 radiographs were used for training (50%) and testing (50%) the DL process. The mAP@0.5 metric is used for performance evaluation of DL considering periapical/bitewing and screws/crowns in upper and lower jaws, with scores ranging from 0.537 to 0.898 (sufficient because DL only needs an approximation). The IU performance is assessed with 50% of the testing radiographs through the t test statistical method, obtaining p values of 0.0106 (line fitting) and 0.0213 (significant point detection). The IU performance is satisfactory, as these values are in accordance with the statistical average/standard deviation in pixels for line fitting (2.75/1.01) and for significant point detection (2.63/1.28) according to the expert criteria of dentists, who establish the ground-truth lines and significant points. In conclusion, AI methods have good prospects for automatic bone loss detection in intraoral radiographs to assist dental specialists in diagnosing peri-implantitis.

Effect of data size on tooth numbering performance via artificial intelligence using panoramic radiographs

OBJECTIVE

This study aims to investigate the effect of number of data on model performance, for the detection of tooth numbering problem on dental panoramic radiographs, with the help of image processing and deep learning algorithms.

STUDY DESIGN

The data set consists of 3000 anonymous dental panoramic X-rays of adult individuals. Panoramic X-rays were labeled on the basis of 32 classes in line with the FDI tooth numbering system. In order to examine the relationship between the number of data used in image processing algorithms and model performance, four different datasets which include 1000, 1500, 2000 and 2500 panoramic X-rays, were used. The training of the models was carried out with the YOLOv4 algorithm and trained models were tested on a fixed test dataset with 500 data and compared based on F1 score, mAP, sensitivity, precision and recall metrics.

RESULTS

The performance of the model increased as the number of data used during the training of the model increased. Therefore, the last model trained with 2500 data showed the highest success among all the trained models.

CONCLUSION

Dataset size is important for dental enumeration, and large samples should be considered as more reliable.

Poultry fecal imagery dataset for health status prediction: A case of South-West Nigeria

Feces is one quick way to determine the health status of the birds and farmers rely on years of experience as well as professionals to identify and diagnose poultry diseases. Most often, farmers lose their flocks as a result of delayed diagnosis or a lack of trustworthy experts. Prevalent diseases affecting poultry birds may be quickly noticed from image of poultry bird's droppings using artificial intelligence based on computer vision and image analysis. This paper provides description of a dataset of both healthy and unhealthy poultry fecal imagery captured from selected poultry farms in south-west of Nigeria using smartphone camera. The dataset was collected at different times of the day to account for variability in light intensity and can be applied in machine learning models development for abnormality detection in poultry farms. The dataset collected is 19,155 images; however, after preprocessing which encompasses cleaning, segmentation and removal of duplicates, the data strength is 14,618 labeled images. Each image is 100 by 100 pixels size in jpeg format. Additionally, computer vision applications like picture segmentation, object detection, and classification can be supported by the dataset. This dataset's creation is intended to aid in the creation of comprehensive tools that will aid farmers and agricultural extension agents in managing poultry farms in an effort to minimize loss and, as a result, optimize profit as well as the sustainability of protein sources.

Is the optimal Tmax threshold identifying perfusion deficit volumes variable across MR perfusion software packages? A pilot study

PURPOSE

Accurate quantification of ischemic core and ischemic penumbra is mandatory for late-presenting acute ischemic stroke. Substantial differences between MR perfusion software packages have been reported, suggesting that the optimal Time-to-Maximum (Tmax) threshold may be variable. We performed a pilot study to assess the optimal Tmax threshold of two MR perfusion software packages (A: RAPID®; B: OleaSphere®) by comparing perfusion deficit volumes to final infarct volumes as ground truth.

METHODS

The HIBISCUS-STROKE cohort includes acute ischemic stroke patients treated by mechanical thrombectomy after MRI triage. Mechanical thrombectomy failure was defined as a modified thrombolysis in cerebral infarction score of 0. Admission MR perfusion were post-processed using two packages with increasing Tmax thresholds (≥ 6 s, ≥ 8 s and ≥ 10 s) and compared to final infarct volume evaluated with day-6 MRI.

RESULTS

Eighteen patients were included. Lengthening the threshold from ≥ 6 s to ≥ 10 s led to significantly smaller perfusion deficit volumes for both packages. For package A, Tmax ≥ 6 s and ≥ 8 s moderately overestimated final infarct volume (median absolute difference: - 9.5 mL, interquartile range (IQR) [- 17.5; 0.9] and 0.2 mL, IQR [- 8.1; 4.8], respectively). Bland-Altman analysis indicated that they were closer to final infarct volume and had narrower ranges of agreement compared with Tmax ≥ 10 s. For package B, Tmax ≥ 10 s was closer to final infarct volume (median absolute difference: - 10.1 mL, IQR: [- 17.7; - 2.9]) versus - 21.8 mL (IQR: [- 36.7; - 9.5]) for Tmax ≥ 6 s. Bland-Altman plots confirmed these findings (mean absolute difference: 2.2 mL versus 31.5 mL, respectively).

CONCLUSIONS

The optimal Tmax threshold for defining the ischemic penumbra appeared to be most accurate at ≥ 6 s for package A and ≥ 10 s for package B. This implies that the widely recommended Tmax threshold ≥ 6 s may not be optimal for all available MRP software package. Future validation studies are required to define the optimal Tmax threshold to use for each package.

BDMediLeaves: A leaf images dataset for Bangladeshi medicinal plants identification

This paper introduces a newly curated dataset named "BDMediLeaves" that includes a diverse collection of leaf images of ten distinct medicinal plants from various regions in Dhaka, Bangladesh. The ten distinct categories are Phyllanthus emblica, Terminalia arjuna, Kalanchoe pinnata, Centella asiatica, Justicia adhatoda, Mikania micrantha, Azadirachta indica, Hibiscus rosa-sinensis, Ocimum tenuiflorum, and Calotropis gigantea. The dataset contains a total of 2,029 original leaf images, along with an additional 38,606 augmented images. Each original image was meticulously captured under natural lighting conditions with an appropriate background. Experts provided accurate labeling for each image, ensuring its seamless integration into various machine learning (ML) and deep learning (DL) models. This comprehensive dataset holds immense potential for researchers in utilizing various ML and DL methods to make significant advancements in the healthcare and pharmaceutical sectors. It serves as a valuable resource for future investigations, laying the foundation for crucial developments in these domains.

Design of experiment and machine learning inform on the 3D printing of hydrogels for biomedical applications

In the biomedical field, 3D printing has the potential to deliver on some of the promises of personalized therapy, notably by enabling point-of-care fabrication of medical devices, dosage forms and bioimplants. To achieve this full potential, a better understanding of the 3D printing processes is necessary, and non-destructive characterization methods must be developed. This study proposes methodologies to optimize the 3D printing parameters for soft material extrusion. We hypothesize that combining image processing with design of experiment (DoE) analyses and machine learning could help obtaining useful information from a quality-by-design perspective. Herein, we investigated the impact of three critical process parameters (printing speed, printing pressure and infill percentage) on three critical quality attributes (gel weight, total surface area and heterogeneity) monitored with a non-destructive methodology. DoE and machine learning were combined to obtain information on the process. This work paves the way for a rational approach to optimize 3D printing parameters in the biomedical field.

Digital image processing realized by memristor-based technologies

Today performance and operational efficiency of computer systems on digital image processing are exacerbated owing to the increased complexity of image processing. It is also difficult for image processors based on complementary metal-oxide-semiconductor (CMOS) transistors to continuously increase the integration density, causing by their underlying physical restriction and economic costs. However, such obstacles can be eliminated by non-volatile resistive memory technologies (known as memristors), arising from their compacted area, speed, power consumption high efficiency, and in-memory computing capability. This review begins with presenting the image processing methods based on pure algorithm and conventional CMOS-based digital image processing strategies. Subsequently, current issues faced by digital image processing and the strategies adopted for overcoming these issues, are discussed. The state-of-the-art memristor technologies and their challenges in digital image processing applications are also introduced, such as memristor-based image compression, memristor-based edge and line detections, and voice and image recognition using memristors. This review finally envisages the prospects for successful implementation of memristor devices in digital image processing.

INSOMNet: Automated insomnia detection using scalogram and deep neural networks with ECG signals

Sleep is a natural state of rest for the body and mind. It is essential for a human's physical and mental health because it helps the body restore itself. Insomnia is a sleep disorder that causes difficulty falling asleep or staying asleep and can lead to several health problems. Conventional sleep monitoring and insomnia detection systems are expensive, laborious, and time-consuming. This is the first study that integrates an electrocardiogram (ECG) scalogram with a convolutional neural network (CNN) to develop a model for the accurate measurement of the quality of sleep in identifying insomnia. Continuous wavelet transform has been employed to convert 1-D time-domain ECG signals into 2-D scalograms. Obtained scalograms are fed to AlexNet, MobileNetV2, VGG16, and newly developed CNN for automated detection of insomnia. The proposed INSOMNet system is validated on the cyclic alternating pattern (CAP) and sleep disorder research center (SDRC) datasets. Six performance measures, accuracy (ACC), false omission rate (FOR), sensitivity (SEN), false discovery rate (FDR), specificity (SPE), and threat score (TS), have been calculated to evaluate the developed model. Our developed system attained the classifications ACC of 98.91%, 98.68%, FOR of 1.5, 0.66, SEN of 98.94%, 99.31%, FDR of 0.80, 2.00, SPE of 98.87%, 98.08%, and TS 0.98, 0.97 on CAP and SDRC datasets, respectively. The developed model is less complex and more accurate than transfer-learning networks. The prototype is ready to be tested with a huge dataset from diverse centers.

A hybrid approach for diagnosing diabetic retinopathy from fundus image exploiting deep features

One of the major causes of blindness in human beings is the diabetic retinopathy (DR). To prevent blindness, early detection of DR is therefore necessary. In this paper, a hybrid model is proposed for diagnosing DR from fundus images. A combination of morphological image processing and Inception v3 deep learning techniques are exploited to detect DR as well as to classify healthy, mild non-proliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR (PDR). The proposed algorithm was carried out in several steps such as segmentation of blood vessels, localization and removal of optic disc, and macula, abnormal features detection (microaneurysms, hemorrhages, and neovascularization), and classification. Microaneurysms and hemorrhages that appear in the retina are the early signs of DR. In this work, we have detected microaneurysms and hemorrhages by applying dynamic contrast limited adaptive histogram equalization and threshold value on overlapping patched images. An overall accuracy of 96.83% is obtained to classify DR into five different stages. The better performance demonstrates the effectiveness and novelty of the proposed work as compared to the recent reported work.

Online citizen science with the Zooniverse for analysis of biological volumetric data

Public participation in research, also known as citizen science, is being increasingly adopted for the analysis of biological volumetric data. Researchers working in this domain are applying online citizen science as a scalable distributed data analysis approach, with recent research demonstrating that non-experts can productively contribute to tasks such as the segmentation of organelles in volume electron microscopy data. This, alongside the growing challenge to rapidly process the large amounts of biological volumetric data now routinely produced, means there is increasing interest within the research community to apply online citizen science for the analysis of data in this context. Here, we synthesise core methodological principles and practices for applying citizen science for analysis of biological volumetric data. We collate and share the knowledge and experience of multiple research teams who have applied online citizen science for the analysis of volumetric biological data using the Zooniverse platform ( www.zooniverse.org ). We hope this provides inspiration and practical guidance regarding how contributor effort via online citizen science may be usefully applied in this domain.

Robot-assisted endoscopic third ventriculostomy under intraoperative CT imaging guidance

BACKGROUND

The robot-assisted neurosurgical procedures have recently benefited of the evolution of intraoperative imaging, including mobile CT unit available in the operating room. This facilitated use paved the way to perform more neurosurgical procedures under robotic assistance. Endoscopic third ventriculocisternostomy requires both a safe transcortical trajectory and a smooth manipulation.

METHOD

We describe our technique of robot-assisted endoscopic third ventriculocisternostomy combining robotic assistance and intraoperative CT imaging.

CONCLUSION

Robot-assisted endoscopic third ventriculocisternostomy using modern intraoperative neuroimaging can be easily implemented and prevented erroneous trajectory and abrupt endoscopic movements, reducing surgically induced brain damages.

Thalamic asymmetry in Multiple Sclerosis

BACKGROUND

Multiple Sclerosis (MS) is a chronic neuroinflammatory disease that affects the central nervous system. Asymmetry is one of the finding in brain MRI of these patients, which is related to the debilitating symptoms of the disease. This study aimed to investigate and compare the thalamic asymmetry in MS patients and its relationship with other MRI and clinical findings of these patients.

METHODS

This cross-sectional study conducted on 83 patients with relapse-remitting MS (RRMS), 43 patients with secondary progressive MS (SPMS), and 89 healthy controls. The volumes of total intracranial, total gray matter, total white matter, lesions, thalamus, and also the thalamic asymmetry indices were calculated. The 9-hole peg test (9-HPT) and Expanded Disability Status Scale (EDSS) were assessed as clinical findings.

RESULTS

We showed that the normalized whole thalamic volume in healthy subjects was higher than MS patients (both RRMS and SPMS). Thalamic asymmetry index (TAI) was significantly different between RRMS patients and SPMS patients (p = 0.011). The absolute value of TAI was significantly lower in healthy subjects than in RRMS (p < 0.001) and SPMS patients (p < 0.001), and SPMS patients had a higher absolute TAI compared to RRMS patients (p = 0.037).

CONCLUSIONS

In this cross-sectional study we showed a relationship between normalized whole thalamic volume and MS subtype. Also, we showed that the asymmetric indices of the thalamus can be related to the progression of the disease. Eventually, we showed that thalamic asymmetry can be related to the disease progression and subtype changes in MS.

Partnering with health sciences libraries to address challenges in bioimaging data management and sharing

Federal mandates, publishing requirements, and an interest in open science have all generated renewed attention on research data management and, in particular, data sharing practices. Due to the size and types of data they produce, bioimaging researchers confront specific challenges in aligning their data with FAIR principles, ensuring that it is findable, accessible, interoperable, and reusable. Although not always recognized by researchers, libraries can, and have been, offering support for data throughout its lifecycle by assisting with data management planning, acquisition, processing and analysis, and sharing and reuse of data. Libraries can educate researchers on best practices for research data management and sharing, facilitate connections to experts by coordinating sessions using peer educators and appropriate vendors, help assess the needs of different researcher groups to identify challenges or gaps, recommend appropriate repositories to make data as accessible as possible, and comply with funder and publisher requirements. As a centralized service within an institution, health sciences libraries have the capability to bridge silos and connect bioimaging researchers with specialized data support across campus and beyond.

Improving behavioral test data collection and analysis in animal models with an image processing program

Behavioral studies are commonly used as a standard procedure to evaluate anxiety and depression in animal models. Recently, different methods have been developed to improve data collection and analysis of the behavioral tests. Currently available methods, including manual analysis and commercially available products, are either time-consuming or costly. The objective of this study was to improve the collection and analysis of behavioral test data in animal models by developing an image processing program. Eleven behavioral parameters were evaluated by three different methods, including (i) manual detection, (ii) commercially available TopScan software (CleverSys Inc, USA), and (iii) In-housed-developed Advanced Move Tracker (AMT) software. Results obtained from different methods were compared to validate the accuracy and efficiency of AMT. Results showed that AMT software provides highly accurate and reliable data analysis compared to other methods. Less than 5% tolerance was reported between results obtained from AMT compared to TopScan. In addition, the analysis processing time was remarkably reduced (68.3%) by using AMT compared to manual detection. Overall, the findings confirmed that AMT is an efficient program for automated data analysis, significantly enhancing research outcomes through accurate analysis of behavioral test data in animal models.

Gibbs Artifacts Removal with Nonlinearity

Background

Gibbs artifacts, appearing as oscillations or ringing around sharp edges or boundaries, are frequently encountered in image processing. They arise when the image's frequency components are adjusted, such as in image deblurring and sharpening. Linear methods are ineffective in reducing Gibbs artifacts; nonlinear methods may be more effective.

Methods

One such nonlinear method is the use of neural networks. This paper applies a simple convolutional neural network (CNN) to an image sharpening task and observes the effects of Gibbs artifacts. This network has only one convolutional layer, which consists of four channels. The well-known rectified linear unit (ReLU) is used as the nonlinear activation function.

Results

For simple one-dimensional (1D) and two-dimensional (2D), unrealistic case studies, the Gibbs artifacts are completely removed. The reason why the artifacts can be removed is explained.

Conclusions

This simple case study illustrates the power of nonlinear functions and the use of multiple channels. In fact, this task can be achieved without using a neural network.

New hybrid segmentation algorithm: UNet-GOA

The U-Net architecture is a prominent technique for image segmentation. However, a significant challenge in utilizing this algorithm is the selection of appropriate hyperparameters. In this study, we aimed to address this issue using an evolutionary approach. We conducted experiments on four different geometric datasets (triangle, kite, parallelogram, and square), with 1,000 training samples and 200 test samples. Initially, we performed image segmentation without the evolutionary approach, manually adjusting the U-Net hyperparameters. The average accuracy rates for the geometric images were 0.94463, 0.96289, 0.96962, and 0.93971, respectively. Subsequently, we proposed a hybrid version of the U-Net architecture, incorporating the Grasshopper Optimization Algorithm (GOA) for an evolutionary approach. This method automatically discovered the optimal hyperparameters, resulting in improved image segmentation performance. The average accuracy rates achieved by the proposed method were 0.99418, 0.99673, 0.99143, and 0.99946, respectively, for the geometric images. Comparative analysis revealed that the proposed UNet-GOA approach outperformed the traditional U-Net architecture, yielding higher accuracy rates.

A new effective method for identifying boletes species based on FT-MIR and three dimensional correlation spectroscopy projected image processing

This study proposed the necessity of identifying the species for boletes in combination with the medicinal value, nutritional value and the problems existing in the industrial development of boletes. Based on the preprocessing of Fourier transform mid-infrared spectroscopy (FT-MIR) by 1st, 2nd, SNV, 2nd + MSC and 2nd + SG, Multilayer Perceptron (MLP) and CatBoost models were established. To avoid complex preprocessing and feature extraction, we try deep learning modeling methods based on image processing. In this paper, the concept of three-dimensional correlation spectroscopy (3DCOS) projection image was proposed, and 9 datasets of synchronous, asynchronous and integrative images are generated by computer method. In addition, 18 deep learning models were established for 9 image datasets with different sizes. The results showed that the accuracy of the three types of synchronous spectral models reached 100%, while the accuracy of the asynchronous spectral and integrative spectral models of 3DCOS projection images were 96.97% and 97.98% in the case of big datasets, which overcame the defects of poor modeling effect of asynchronous spectral and integrative spectral in previous two-dimensional correlation spectroscopy (2DCOS) studies. In conclusion, the modeling results of 3DCOS projection images are perfect, and we can apply this method to other identification fields in the future.

Human tracking robotic camera based on image processing for live streaming of conferences and seminars

There are numerous scenarios where the photographer is in difficulty and unable to capture or shoot video as required. This could be due to several factors such as limited space, decreased visibility, and an obstacle in the way. Therefore, this project implements the idea to capture and shoot video of the desired subject through an automatically controlled robotic camera with no need for a photographic bloke. The system comprises functions such as detection, tracking, live streaming, and video/audio recording along with the features of Radio-Frequency-Identification (RFID). Therefore, this robotic camera will detect the desired subject, track and focus it with the help of its position driven through movable motors sensing the RFID tag in case the object is non-stationary. The video/audio will be recorded on a computer along with the live streaming available on an Android-based device. The Viola-Jones algorithm of the image processing technique is used to detect the particular subject features and C for accessing the movable camera protocols. The RFID transmitter and receiver are used to sense the RFID card and serve the purpose to track the subject using the algorithms of image processing, with the advantage of ignoring other obstacles between the camera and the detected subject. Thus, adding a novel functionality to the existing systems, that lacks the feature of focusing the camera on the subject, when an obstacle is detected in between. The live streaming is achieved wirelessly through an open-source platform X-operating system, Apache, MySQL, Php, Perl (XAMPP). The idea is verified through concluded arrangements in self-made scenarios in response to the speed, distance, light, and background noise of the detected subject, which delivered encouraging results. Therefore, the designed system can be used for live conferences, seminars, and other multimedia-required arrangements.

Carimas: An Extensive Medical Imaging Data Processing Tool for Research

Carimas is a multi-purpose medical imaging data processing tool, which can be used to visualize, analyze, and model different medical images in research. Originally, it was developed only for positron emission tomography data in 2009, but the use of this software has extended to many other tomography imaging modalities, such as computed tomography and magnetic resonance imaging. Carimas is especially well-suited for analysis of three- and four-dimensional image data and creating polar maps in modeling of cardiac perfusion. This article explores various parts of Carimas, including its key features, program structure, and application possibilities.

Segmentation and counting of wheat spike grains based on deep learning and textural feature

BACKGROUND

Grain count is crucial to wheat yield composition and estimating yield parameters. However, traditional manual counting methods are time-consuming and labor-intensive. This study developed an advanced deep learning technique for the segmentation counting model of wheat grains. This model has been rigorously tested on three distinct wheat varieties: 'Bainong 307', 'Xinmai 26', and 'Jimai 336', and it has achieved unprecedented predictive counting accuracy.

METHOD

The images of wheat ears were taken with a smartphone at the late stage of wheat grain filling. We used image processing technology to preprocess and normalize the images to 480*480 pixels. A CBAM-HRNet wheat grain segmentation counting deep learning model based on the Convolutional Block Attention Module (CBAM) was constructed by combining deep learning, migration learning, and attention mechanism. Image processing algorithms and wheat grain texture features were used to build a grain counting and predictive counting model for wheat grains.

RESULTS

The CBAM-HRNet model using the CBAM was the best for wheat grain segmentation. Its segmentation accuracy of 92.04%, the mean Intersection over Union (mIoU) of 85.21%, the category mean pixel accuracy (mPA) of 91.16%, and the recall rate of 91.16% demonstrate superior robustness compared to other models such as HRNet, PSPNet, DeeplabV3+ , and U-Net. Method I for spike count, which calculates twice the number of grains on one side of the spike to determine the total number of grains, demonstrates a coefficient of determination R² of 0.85, a mean absolute error (MAE) of 1.53, and a mean relative error (MRE) of 2.91. In contrast, Method II for spike count involves summing the number of grains on both sides to determine the total number of grains, demonstrating a coefficient of determination R² of 0.92, an MAE) of 1.15, and an MRE) of 2.09%.

CONCLUSIONS

Image segmentation algorithm of the CBAM-HRNet wheat spike grain is a powerful solution that uses the CBAM to segment wheat spike grains and obtain richer semantic information. This model can effectively address the challenges of small target image segmentation and under-fitting problems in training. Additionally, the spike grain counting model can quickly and accurately predict the grain count of wheat, providing algorithmic support for efficient and intelligent wheat yield estimation.

Improving performance of medical image alignment through super-resolution

Medical image alignment is an important tool for tracking patient conditions, but the quality of alignment is influenced by the effectiveness of low-dose Cone-beam CT (CBCT) imaging and patient characteristics. To address these two issues, we propose an unsupervised alignment method that incorporates a preprocessing super-resolution process. We constructed the model based on a private clinical dataset and validated the enhancement of the super-resolution on alignment using clinical and public data. Through all three experiments, we demonstrate that higher resolution data yields better results in the alignment process. To fully constrain similarity and structure, a new loss function is proposed; Pearson correlation coefficient combined with regional mutual information. In all test samples, the newly proposed loss function obtains higher results than the common loss function and improve alignment accuracy. Subsequent experiments verified that, combined with the newly proposed loss function, the super-resolution processed data boosts alignment, can reaching up to 9.58%. Moreover, this boost is not limited to a single model, but is effective in different alignment models. These experiments demonstrate that the unsupervised alignment method with super-resolution preprocessing proposed in this study effectively improved alignment and plays an important role in tracking different patient conditions over time.

Optimization of CBCT data with image processing methods and production with fused deposition modeling 3D printing

The present study has investigated the effect of the removal of artifacts in cone beam computed tomography (CBCT) images with image processing techniques to dental implant planning. The aim of this study has been to benefit from the novel image processing techniques and additive manufacturing technologies in order to change the existing approach in the usage of the 3D model in the orthogonal surgery, traumatic cases, and tumor operations and to solve the restrictions in surgical operations. In the study, firstly, 3 × 3, 5 × 5, and 7 × 7 kernel values were determined on the CBCT image data of the patient. The determined kernel values were applied on CBCT images by choosing median, median-mean-Gaussian (MMG), and bilateral filters, which are quite successful in removing noise in medical images. A thresholding process to separate teeth and bones from soft tissue regions on CBCT images, histogram normalization for a balanced color distribution, morphology operations to reduce noise areas, and tooth and bone boundaries were determined as closely as possible to patient anatomy. The original image and the images obtained from image enhancement techniques were compared. Results showed that the 3 × 3 median filtering method from three different kernel values out of three different image processing methods used in the study greatly improved the artifacts. It has also been shown that the availability of image processing and additive manufacturing methods on CBCT images has been shown to be a highly important factor before dental surgery planning.

Resolution enhancement with a task-assisted GAN to guide optical nanoscopy image analysis and acquisition

Super-resolution fluorescence microscopy methods enable the characterization of nanostructures in living and fixed biological tissues. However, they require the adjustment of multiple imaging parameters while attempting to satisfy conflicting objectives, such as maximizing spatial and temporal resolution while minimizing light exposure. To overcome the limitations imposed by these trade-offs, post-acquisition algorithmic approaches have been proposed for resolution enhancement and image-quality improvement. Here we introduce the task-assisted generative adversarial network (TA-GAN), which incorporates an auxiliary task (for example, segmentation, localization) closely related to the observed biological nanostructure characterization. We evaluate how the TA-GAN improves generative accuracy over unassisted methods, using images acquired with different modalities such as confocal, bright-field, stimulated emission depletion and structured illumination microscopy. The TA-GAN is incorporated directly into the acquisition pipeline of the microscope to predict the nanometric content of the field of view without requiring the acquisition of a super-resolved image. This information is used to automatically select the imaging modality and regions of interest, optimizing the acquisition sequence by reducing light exposure. Data-driven microscopy methods like the TA-GAN will enable the observation of dynamic molecular processes with spatial and temporal resolutions that surpass the limits currently imposed by the trade-offs constraining super-resolution microscopy.

The SWAP Filter: A Simple Azimuthally Varying Radial Filter for Wide-Field EUV Solar Images

We present the SWAP Filter: an azimuthally varying, radial normalizing filter specifically developed for EUV images of the solar corona, named for the Sun Watcher with Active Pixels and Image Processing (SWAP) instrument on the Project for On-Board Autonomy 2 (PROBA2) spacecraft. We discuss the origins of our technique, its implementation and key user-configurable parameters, and highlight its effects on data via a series of examples. We discuss the filter's strengths in a data environment in which wide field-of-view observations that specifically target the low signal-to-noise middle corona are newly available and expected to grow in the coming years.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11207-023-02183-w.

VolumePeeler: a novel FIJI plugin for geometric tissue peeling to improve visualization and quantification of 3D image stacks

MOTIVATION

Quantitative descriptions of multi-cellular structures from optical microscopy imaging are prime to understand the variety of three-dimensional (3D) shapes in living organisms. Experimental models of vertebrates, invertebrates and plants, such as zebrafish, killifish, Drosophila or Marchantia, mainly comprise multilayer tissues, and even if microscopes can reach the needed depth, their geometry hinders the selection and subsequent analysis of the optical volumes of interest. Computational tools to "peel" tissues by removing specific layers and reducing 3D volume into planar images, can critically improve visualization and analysis.

RESULTS

We developed VolumePeeler, a versatile FIJI plugin for virtual 3D "peeling" of image stacks. The plugin implements spherical and spline surface projections. We applied VolumePeeler to perform peeling in 3D images of spherical embryos, as well as non-spherical tissue layers. The produced images improve the 3D volume visualization and enable analysis and quantification of geometrically challenging microscopy datasets.

AVAILABILITY

ImageJ/FIJI software, source code, examples, and tutorials are openly available in https://cimt.uchile.cl/mcerda.

Interrater reliability for the detection of cortical lesions on phase-sensitive inversion recovery magnetic resonance imaging in patients with multiple sclerosis

Objective

To assess the reliability of phase-sensitive inversion recovery (PSIR) magnetic resonance imaging (MRI) and its accuracy for determining the topography of demyelinating cortical lesions in patients with multiple sclerosis (MS).

Materials and Methods

This was a cross-sectional study conducted at a tertiary referral center for MS and other demyelinating disorders. We assessed the agreement among three raters for the detection and topographic classification of cortical lesions on fluid-attenuated inversion recovery (FLAIR) and PSIR sequences in patients with MS.

Results

We recruited 71 patients with MS. The PSIR sequences detected 50% more lesions than did the FLAIR sequences. For detecting cortical lesions, the level of interrater agreement was satisfactory, with a mean free-response kappa (κFR) coefficient of 0.60, whereas the mean κFR for the topographic reclassification of the lesions was 0.57. On PSIR sequences, the raters reclassified 366 lesions (20% of the lesions detected on FLAIR sequences), with excellent interrater agreement. There was a significant correlation between the total number of lesions detected on PSIR sequences and the Expanded Disability Status Scale score (ρ = 0.35; p < 0.001).

Conclusion

It seems that PSIR sequences perform better than do FLAIR sequences, with clinically satisfactory interrater agreement, for the detection and topographic classification of cortical lesions. In our sample of patients with MS, the PSIR MRI findings were significantly associated with the disability status, which could influence decisions regarding the treatment of such patients.

A new automatic method for tracking rats in the Morris water maze

Morris water maze (MWM) test is widely used to evaluate the learning and memory deficits in rodents. Image processing and pattern recognition can be used to analyse videos and recognize automatically the tracking in MWM. There are several commercial and free access software that allows analyzing the behavioral tasks although they also have limitations such as automation, cost, user intervention among other things. The aim of this paper was to develop a new image processing technique to automatically analyse the track of the rat in the MWM, which we called RatsTrack. The MWM test was performed with an animal model for Alzheimer, and the videos were recorded to measure the distance, time, and speed. The segmentation method based on the projection of the video frames was made for pool identification, eliminating the rat, while conserving the shape of the pool. Then, the Hough transformation was used to recognize the position and radius of the pool. Finally, the frame in which the rat is released into the pool was established automatically using mathematical morphology techniques and added as a plugin on free access ImageJ software. The new image processing technique, RatsTrack, successfully detected and located the pool and rat without user intervention, significantly decreasing operational time and providing results for distance, time, speed, and acceleration parameters of the MWM test. Alzheimer's rats compared with the control group presented significant data measured with the RatsTrack. RatsTrack is a plugin of ImageJ software and will be made freely available for public use.

Quantitative analysis validation for sclerotherapy treatment of lower limb telangiectasias

BACKGROUND

The evaluation of sclerotherapy efficacy for lower limb telangiectasias, which is the standard treatment for such condition, is commonly assisted by scores based on before and after pictures. This method is marked by its subjectivity, which impairs the precision of studies on the subject, making it unfeasible to evaluate and compare different interventions. We hypothesize that a quantitative method for evaluating the effectiveness of sclerotherapy for lower limb telangiectasias may present more reproducible results. Reliable measurement methods and new technologies may become part of the clinical practice in the near future.

METHODS

Before and after treatment photographs were analyzed using a quantitative method and compared with a validated qualitative method based on improvement scores. Reliability analysis of the methods was performed, applying the intraclass correlation coefficient (ICC) and kappa coefficient with quadratic weights (Fleiss Cohen), for analysis of inter-examiner and intra-examiner agreement in both evaluation methods. Convergent validity was evaluated by applying the Spearman test. To assess the applicability of the quantitative scale, the Mann-Whitney test was used.

RESULTS

A better agreement between examiners is shown for the quantitative scale, with a mean kappa of .3986 (.251-.511) for qualitative analysis and a mean kappa of .788 (.655-.918) for quantitative analysis (P < .001 for all examiners). Convergent validity was achieved by correlation coefficients of .572 to .905 (P < .001). The quantitative scale results obtained between the specialists with different degrees of experience did not show statistical difference (seniors: 0.71 [-0.48/1.00] × juniors: 0.73 [-0.34/1.00]; P = .221).

CONCLUSIONS

Convergent validity between both analyses has been achieved, but quantitative analysis has been shown to be more reliable and can be applied by professionals of any degree of experience. The validation of quantitative analysis is a major milestone for the development of new technology and automated, reliable, applications.

Shoeprint image retrieval and crime scene shoeprint image linking by using convolutional neural network and normalized cross correlation

A shoeprint image retrieval process aims to identify and match images of shoeprints found at crime scenes with shoeprint images from a known reference database. It is a challenging problem in the forensic discipline of footwear analysis because a shoeprint found at the crime scene is often imperfect. Recovered shoeprints may be partial, distorted, left on surfaces that do not mark easily, or perhaps come from shoes that do not transfer marks easily. In this study, we present a shoeprint retrieval method by using a convolutional neural network (CNN) and normalized cross-correlation (NCC). A pre-trained CNN was used to extract features from the pre-processed shoeprint images. We then employed NCC to compute a similarity score based on the extracted image features. We achieved a retrieval accuracy of 82% in our experiments, where a "successful" retrieval means that the ground truth image was returned in the top 1% of returned images. We also extend our shoeprint retrieval method to the problem of linking shoeprints recovered from crime scenes. This new method can provide a linkage between two crime scenes if the two recovered shoeprints originated from the same shoe. This new method achieved a retrieval accuracy of 88.99% in the top 20% of returned images.

A computational suite for the structural and functional characterization of amyloid aggregates

We developed the aggregate characterization toolkit (ACT), a fully automated computational suite based on existing and widely used core algorithms to measure the number, size, and permeabilizing activity of recombinant and human-derived aggregates imaged with diffraction-limited and super-resolution microscopy methods at high throughput. We have validated ACT on simulated ground-truth images of aggregates mimicking those from diffraction-limited and super-resolution microscopies and showcased its use in characterizing protein aggregates from Alzheimer's disease. ACT is developed for high-throughput batch processing of images collected from multiple samples and is available as an open-source code. Given its accuracy, speed, and accessibility, ACT is expected to be a fundamental tool in studying human and non-human amyloid intermediates, developing early disease stage diagnostics, and screening for antibodies that bind toxic and heterogeneous human amyloid aggregates.

Dynamics of carbon dioxide emission during cracking in peanut shell biochar-amended soil

As a primary source of greenhouse gas emissions and a carbon sink, soil plays a key role in climate regulation. The development of cracks in soil strongly influences CO₂ emissions, and soil amendment with biochar has been shown to reduce cracking. However, the impact of biochar on CO₂ emissions during soil cracking is not well understood. This study investigates the release of CO₂ flux during the cracking of peanut shell biochar-amended soil. The biochar-amended soil was incubated at a constant temperature of 35 °C for 160 h with periodic photography and analysis of CO₂ concentration and soil moisture. To achieve continuous monitoring of incubation soil, a new coupled sensor was specially designed to measure CO₂ concentration and soil moisture, based on the Arduino microcontroller. Measured results reveal that peanut shell biochar reduced the evaporation rate by 29 % compared to unamended soil, resulting in slower soil cracking caused by water loss. The biochar also decreased the shrinkage crack length by 20 % compared to unamended soil. In addition, the crack volume fraction was reduced by 16 % after the peanut shell biochar amendment. Due to the reduction of the soil crack channel openings during drying shrinkage when biochar was applied to the soil, cumulative CO₂ fluxes were also reduced by 5 % compared to unamended soil. The presence of biochar induced more stable and larger compounds with the soil particles, which blocked the crack propagation path and inhibited further development of the crack.

A comparative analysis of growth kinetics, image analysis, and biofuel potential of different algal strains

Due to the global population growth and economic development, energy demand has increased worldwide. Countries take steps to improve their alternative and renewable energy sources. Algae is one of the alternative energy sources and can be used to produce renewable biofuel. In this study, nondestructive, practical, and rapid image processing techniques were applied to determine the algal growth kinetics and biomass potential of four algal strains, including C. minutum, Chlorella sorokiniana, C. vulgaris, and S. obliquus. Laboratory experiments were conducted to determine different aspects of biomass and chlorophyll production of those algal strains. Suitable non-linear growth models, including Logistic, modified Logistic, Gompertz, and modified Gompertz models, were employed to determine the growth pattern of algae. Moreover, the methane potential of harvested biomass was calculated. The algal strains were incubated for 18 days, and the growth kinetics were determined. After the incubation, the biomass was harvested and assessed for its chemical oxygen demand content and biomethane potential. Among the tested strains, C. sorokiniana was the best in biomass productivity (111.97 ± 0.9 mg L^-1d^-1). The calculated vegetation indices, namely; colorimetric difference, color index vegetation, vegetative, excess green, excess green minus excess red, combination, and brown index values showed a significant correlation with biomass and chlorophyll content. Among the tested growth models, the modified Gompertz shows the best growth pattern. Further, the estimated theoretical CH₄ yield was highest for C. minutum (0.98 mL g^-1) compared to other tested strains. The present findings suggest that image analysis can be used as an alternative method to study the growth kinetics and biomass production potential of different algae during cultivation in wastewater.

Radiomic applications in upper gastrointestinal cancer surgery

INTRODUCTION

Cross-sectional imaging plays an integral role in the management of upper gastrointestinal (UGI) cancer, from initial diagnosis and staging to determining appropriate treatment strategies. Subjective imaging interpretation has known limitations. The field of radiomics has evolved to extract quantitative data from medical imaging and relate these to biological processes. The key concept behind radiomics is that the high-throughput analysis of quantitative imaging features can provide predictive or prognostic information, with the goal of providing individualised care.

OBJECTIVE

Radiomic studies have shown promising utility in upper gastrointestinal oncology, highlighting a potential role in determining stage of disease and degree of tumour differentiation and predicting recurrence-free survival. This narrative review aims to provide an insight into the concepts underpinning radiomics, as well as its potential applications for guiding treatment and surgical decision-making in upper gastrointestinal malignancy.

CONCLUSION

Outcomes from studies to date have been promising; however, further standardisation and collaboration are required. Large prospective studies with external validation and evaluation of radiomic integration into clinical pathways are needed. Future research should now focus on translating the promising utility of radiomics into meaningful patient outcomes.

Bangladeshi medicinal plant dataset

Medicinal plants have been used to treat diseases since ancient times. Plants used as raw materials for herbal medicine are known as medicinal plants [2]. The U. S. Forest Service estimates that 40% of pharmaceutical drugs in the Western world are derived from plants [1]. Seven thousand medical compounds are derived from plants in the modern pharmacopeia. Herbal medicine combines traditional empirical knowledge with modern science [2]. A medicinal plant is considered an important source of prevention against various diseases [2]. The essential medicine component is extracted from different parts of the plants [8]. In underdeveloped countries, people use medicinal plants as a substitute for medicine. There are various species of plants in the world. Herbs are one of them, which are of different shapes, colors, and leaves [5]. It is difficult for ordinary people to recognize these species of herbs. People use more than 50000 plants in the world for medicinal purposes. There are 8000 medicinal plants in India with evidence of medicinal properties [7]. Automatic classification of these plant species is important because it requires intensive domain knowledge to manually classify the proper species. Machine learning techniques are extensively used in classifying medicinal plant species from photographs, which is challenging but intriguing to academics. Artificial Neural Network classifiers' effective performance depends on the quality of the image dataset [4]. This article represents a medicinal plant dataset: an image dataset of ten different Bangladeshi plant species. Images of medicinal plant leaves were from various gardens, including the Pharmacy Garden at Khwaja Yunus Ali University and the Khwaja Yunus Ali Medical College & Hospital in Sirajganj, Bangladesh. Images were collected by taking pictures with high-resolution mobile phone cameras. Ten medicinal species, 500 images per species are included in the data set, namely, Nayantara (Catharanthus roseus), Pathor kuchi (Kalanchoe pinnata), Gynura procumbens (Longevity spinach), Bohera (Terminalia bellirica), Haritaki (Terminalia chebula), Thankuni (Centella asiatica), Neem (Azadirachta indica), Tulsi (Ocimum tenniflorum), Lemon grass (Cymbopogon citratus), and Devil backbone (Euphorbia tithymaloides). This dataset will benefit researchers applying machine learning and computer vision algorithms in several ways. For example, training and evaluation of machine learning models with this well-curated high-quality dataset, development of new computer vision algorithms, automatic medicinal plant identification in the field of botany and pharmacology for drug discovery and conservation, and data augmentation. Overall, this medicinal plant image dataset can provide researchers in the field of machine learning and computer vision with a valuable resource to develop and evaluate algorithms for plant phenotyping, disease detection, plant identification, drug development, and other tasks related to medicinal plants.

An unique dataset for Christian sacral objects identification

Christian religious monuments as cathedrals, chapels, and temples, are found in many places on our planet. World-famous buildings such as the Notre Dame Cathedral in Paris, Gaudi's Cathedral in Barcelona, and St. Vitus Cathedral in Prague are commonly known. Many online photographs can be used to build machine-learning models to identify them. The number of photographs is already significantly lower for little-known buildings, such as small churches in the Czech-German border region, and similar approaches cannot be used for identification. Based on these facts, our team has compiled a unique dataset for identifying the most important elements of Christian sacral buildings as altars, frescoes, pulpits, etc., which are almost always found in them. Our data set was manually created from several thousand real photographs. This dataset seems to be very usable, e.g., for creating new machine learning models and identifying objects in sacred objects or the objects themselves.

Soybean image dataset for classification

This paper presents a dataset with 5513 images of individual soybean seeds, which encompass five categories: (Ⅰ) Intact, (Ⅱ) Immature, (Ⅲ) Skin-damaged, (Ⅳ) Spotted, and (Ⅴ) Broken. Furthermore, there are over 1000 images of soybean seeds in each category. Those images of individual soybeans were classified into five categories based on the Standard of Soybean Classification (GB1352-2009) [1]. The soybean images with the seeds in physical touch were captured by an industrial camera. Subsequently, individual soybean images (227×227 pixels) were divided from the soybean images (3072×2048 pixels) using an image-processing algorithm with a segmentation accuracy of over 98%. The dataset can serve to study the classification or quality assessment of soybean seeds.

Implementation of Automated Pipeline for Resting-State fMRI Analysis with PACS Integration

In recent years, the quantity and complexity of medical imaging acquisition and processing have increased tremendously. The explosion in volume and need for advanced imaging analysis have led to the creation of numerous software programs, which have begun to be incorporated into clinical practice for indications such as automated stroke assessment, brain tumor perfusion processing, and hippocampal volume analysis. Despite these advances, there remains a need for specialized, custom-built software for advanced algorithms and new areas of research that is not widely available or adequately integrated in these "out-of-the-box" solutions. The purpose of this paper is to describe the implementation of an image-processing pipeline that is versatile and simple to create, which allows for rapid prototyping of image analysis algorithms and subsequent testing in a clinical environment. This pipeline uses a combination of Orthanc server, custom MATLAB code, and publicly available FMRIB Software Library and RestNeuMap tools to automatically receive and analyze resting-state functional MRI data collected from a custom filter on the MR scanner output. The processed files are then sent directly to Picture Archiving and Communications System (PACS) without the need for user input. This initial experience can serve as a framework for those interested in simple implementation of an automated pipeline customized to clinical needs.

Deep learning based detection of monkeypox virus using skin lesion images

As we set into the second half of 2022, the world is still recovering from the two-year COVID-19 pandemic. However, over the past three months, the outbreak of the Monkeypox Virus (MPV) has led to fifty-two thousand confirmed cases and over one hundred deaths. This caused the World Health Organisation to declare the outbreak a Public Health Emergency of International Concern (PHEIC). If this outbreak worsens, we could be looking at the Monkeypox virus causing the next global pandemic. As Monkeypox affects the human skin, the symptoms can be captured with regular imaging. Large samples of these images can be used as a training dataset for machine learning-based detection tools. Using a regular camera to capture the skin image of the infected person and running it against computer vision models is beneficial. In this research, we use deep learning to diagnose monkeypox from skin lesion images. Using a publicly available dataset, we tested the dataset on five pre-trained deep neural networks: GoogLeNet, Places365-GoogLeNet, SqueezeNet, AlexNet and ResNet-18. Hyperparameter was done to choose the best parameters. Performance metrics such as accuracy, precision, recall, f1-score and AUC were considered. Among the above models, ResNet18 was able to obtain the highest accuracy of 99.49%. The modified models obtained validation accuracies above 95%. The results prove that deep learning models such as the proposed model based on ResNet-18 can be deployed and can be crucial in battling the monkeypox virus. Since the used networks are optimized for efficiency, they can be used on performance limited devices such as smartphones with cameras. The addition of explainable artificial intelligence techniques LIME and GradCAM enables visual interpretation of the prediction made, helping health professionals using the model.

Image-based plant wilting estimation

BACKGROUND

Environmental stress due to climate or pathogens is a major threat to modern agriculture. Plant genetic resistance to these stresses is one way to develop more resilient crops, but accurately quantifying plant phenotypic responses can be challenging. Here we develop and test a set of metrics to quantify plant wilting, which can occur in response to abiotic stress such as heat or drought, or in response to biotic stress caused by pathogenic microbes. These metrics can be useful in genomic studies to identify genes and genomic regions underlying plant resistance to a given stress.

RESULTS

We use two datasets: one of tomatoes inoculated with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease, and another of soybeans exposed to water stress. For both tomato and soybean, the metrics predict the visual wilting score provided by human experts. Specific to the tomato dataset, we demonstrate that our metrics can capture the genetic difference of bacterium wilt resistance among resistant and susceptible tomato genotypes. In soybean, we show that our metrics can capture the effect of water stress.

CONCLUSION

Our proposed RGB image-based wilting metrics can be useful for identifying plant wilting caused by diverse stresses in different plant species.