Color clustering segmentation framework for image analysis of malignant lymphoid cells in peripheral blood

Enhancing Bone Cancer Diagnosis Through Image Extraction and Machine Learning: A State-of-the-Art Approach

Background: Bone cancer is a severe condition often leading to patient mortality. Diagnosis relies on X-rays, MRIs, or CT scans, which require time-consuming manual review by experts. Thus, developing an automated system is crucial for accurate classification of malignant and healthy bone.Methods: Differentiating between them poses a challenge as they may exhibit similar physical characteristics. The initial step is selecting the optimal edge detection method. Two feature sets are then generated: one with the histogram of oriented gradients (HOG) and one without. Performance evaluation involves two machine learning models: Support Vector Machine (SVM) and Random Forest.Results: Including HOG consistently yields superior results. The SVM model with HOG achieves an F-1 score of 0.92, outperforming the Random Forest model's .77. This study aims to develop reliable methods for bone cancer classification. The proposed automated method assists surgeons in accurately detecting malignant bone regions using modern image analysis techniques and machine learning models. Incorporating HOG significantly enhances performance, improving differentiation between malignant and healthy bone.Conclusion: Ultimately, this approach supports precise diagnoses and informed treatment decisions for bone cancer patients.

A hybrid cell image segmentation method based on the multilevel improvement of data

Over the years, several methods have been developed for the segmentation of cell images. Most of the related techniques operate directly on the raw data (noisy cell samples) of the medical image which leads to adverse effects on the structure of leucocytes because the medical images are affected by multiple distortions (varying illumination, deficient background light intensity, and non-uniform staining). To overcome these problems, we came up with an improved solution that performs the qualitative enhancement of cell images for the smooth extraction of cell-nucleus. Although various segmentation methods have adopted an image improvement operation in practice. These methods also amplify the magnitude of image noise which leads to over-sampling and under-sampling of data points. This mis-labelling of data points is minimized by the developed approach which adopts a collaborative fusion strategy (CNN and Nuclear-norm approach) for the qualitative improvement of cell images. The enhanced cell samples were forwarded to the U-net (deep learning model) model for the semantic segmentation of cell images. The performance evaluation of the model was performed on three biomedical cell imaging datasets, which include the ALL-IDB (99.89% accuracy, 99.51% recall, and 99.01% precision), CellaVision (99.68% accuracy, 98.75% precision, and 97.94% specificity) and JTSC (98.45% accuracy, 97.42% precision, and 97.21% specificity) dataset. The results were compared with the state-of-art methods in which the adopted hybrid approach has overpowered the related techniques in the quantitative and qualitative domains.

An Automated Segmentation of Leukocytes Using Modified Watershed Algorithm on Peripheral Blood Smear Images

Leukemia can be detected by an abnormal rise in the number of immature lymphocytes and by a decrease in the number of other blood cells. To diagnose leukemia, image processing techniques are utilized to examine microscopic peripheral blood smear (PBS) images automatically and swiftly. To the best of our knowledge, the initial step in subsequent processing is a robust segmentation technique for identifying leukocytes from their surroundings. The paper presents the segmentation of leukocytes in which three color spaces are considered in this study for image enhancement. The proposed algorithm uses a marker-based watershed algorithm and peak local maxima. The algorithm was used on three different datasets with various color tones, image resolutions, and magnifications. The average precision for all three-color spaces was the same, i.e. 94% but the Structural Similarity Index Metric (SSIM) and recall of HSV were better than other two. The results of this study will aid experts in narrowing down their options for segmenting leukemia. Based on the comparison, it was concluded that when the colour space correction technique is used, the accuracy of the proposed methodology improves.

The Application of Morphogo in the Detection of Megakaryocytes from Bone Marrow Digital Images with Convolutional Neural Networks

The evaluation of megakaryocytes is an important part of the work up on bone marrow smear examination. It has significance in the differential diagnosis, therapeutic efficacy assessment, and predication of prognosis of many hematologic diseases. The process of manual identification of megakaryocytes are tedious and lack of reproducibility; therefore, a reliable method of automated megakaryocytic identification is urgently needed. Three hundred and thirty-three bone marrow aspirate smears were digitized by Morphogo system. Pathologists annotated megakaryocytes on the digital images of marrow smears are applied to construct a large dataset for testing the system's predictive performance. Subsequently, we obtained megakaryocyte count and classification for each sample by different methods (system-automated analysis, system-assisted analysis, and microscopic examination) to study the correlation between different counting and classification methods. Morphogo system localized cells likely to be megakaryocytes on digital smears, which were later annotated by pathologists and the system, respectively. The system showed outstanding performance in identifying megakaryocytes in bone marrow smears with high sensitivity (96.57%) and specificity (89.71%). The overall correlation between the different methods was confirmed the high consistency (r ≥ 0.7218, R² ≥ 0.5211) with microscopic examination in classifying megakaryocytes. Morphogo system was proved as a reliable screen tool for analyzing megakaryocytes. The application of Morphogo system shows promises to advance the automation and standardization of bone marrow smear examination.

The Algorithm of Watershed Color Image Segmentation Based on Morphological Gradient

The traditional watershed algorithm has the disadvantage of over-segmentation and interference with an image by reflected light. We propose an improved watershed color image segmentation algorithm. It is based on a morphological gradient. This method obtains the component gradient of a color image in a new color space is not disturbed by the reflected light. The gradient image is reconstructed by opening and closing. Therefore, the final gradient image is obtained. The maximum inter-class variance algorithm is used to obtain the threshold automatically for the final gradient image. The original gradient image is forcibly calibrated with the obtained binary labeled image, and the modified gradient image is segmented by watershed. Experimental results show that the proposed method can obtain an accurate and continuous target contour. It will achieve the minimum number of segmentation regions following human vision. Compared with similar algorithms, this way can suppress the meaningless area generated by the reflected light. It will maintain the edge information of the object well. It will improve the robustness and applicability. From the experimental results, it can be seen that compared with the region-growing method and the automatic threshold method; the proposed algorithm has a great improvement in operation efficiency, which increased by 10%. The accuracy and recall rate of the proposed algorithm is more than 0.98. Through the experimental comparison, the advantages of the proposed algorithm in object segmentation can be more intuitively illustrated.

Optimizing rat and human blood cells sampling for in silico morphometric analysis

Measurements of Morphometric Parameters of the Blood Cells (MPBC) are key for the diagnosis of both mental and metabolic diseases. Several manual approaches or computational methodologies are useful to provide reliable clinical diagnosis. The sample processing and data analysis is relevant, however the sample handling on the pre-analytical phase remains scarcely evaluated. The main goal of this study was to favor the preservation of blood smear using a histological resin. This strategy lead us two practical approaches, give a detailed morphometric description of white blood cells and establish reference intervals in male Wistar rats, which are scarcely reported. Blood smears from male Wistar rats (n = 120) and adult men were collected at room temperature. The integrity of Wright-stained cells was evaluated by an in silico image analysis from rat and human blood smear preserved with a toluene-based synthetic resin mounting medium. A single sample of human blood was used as a control of procedure. The reference intervals was established by cell counting. Based on the results of segmentation algorithm followed by an automatic thresholding analysis, the incorporation of resin favor the conservation of cell blood populations, and lead to identify morphologic features such as nucleus/cytoplasmic shape, granules presence and DNA appearance in nucleus of white blood cells. The use of a histological resin could favor a fast and efficient sample handling in silico MPBC measurements both in the species studied as in wild animals.

A new convolutional neural network predictive model for the automatic recognition of hypogranulated neutrophils in myelodysplastic syndromes

BACKGROUND

Dysplastic neutrophils commonly show at least 2/3 reduction of the content of cytoplasmic granules by morphologic examination. Recognition of less granulated dysplastic neutrophils by human eyes is difficult and prone to inter-observer variability. To tackle this problem, we proposed a new deep learning model (DysplasiaNet) able to automatically recognize the presence of hypogranulated dysplastic neutrophils in peripheral blood.

METHODS

Eight models were generated by varying convolutional blocks, number of layer nodes and fully connected layers. Each model was trained for 20 epochs. The five most accurate models were selected for a second stage, being trained again from scratch for 100 epochs. After training, cut-off values were calculated for a granularity score that discerns between normal and dysplastic neutrophils. Furthermore, a threshold value was obtained to quantify the minimum proportion of dysplastic neutrophils in the smear to consider that the patient might have a myelodysplastic syndrome (MDS). The final selected model was the one with the highest accuracy (95.5%).

RESULTS

We performed a final proof of concept with new patients not involved in previous steps. We reported 95.5% sensitivity, 94.3% specificity, 94% precision, and a global accuracy of 94.85%.

CONCLUSIONS

The primary contribution of this work is a predictive model for the automatic recognition in an objective way of hypogranulated neutrophils in peripheral blood smears. We envision the utility of the model implemented as an evaluation tool for MDS diagnosis integrated in the clinical laboratory workflow.

CNN-SSPSO: A Hybrid and Optimized CNN Approach for Peripheral Blood Cell Image Recognition and Classification

White blood cells (WBCs) play a main role in identifying the health condition and disease characteristics of a normal person. An automated classification system is capable of recognizing white blood cells that may help doctors to diagnose several diseases like malaria, anemia, leukemia, etc. Automated blood cell analysis allows fast and accurate outcomes and often involves broad data without performance negotiation. The state-of-the-art systems use a lot of different stages (feature extraction, segmentation, pre-processing, etc.) to provide the automated blood cell analysis using blood smear images which is a lengthy process. To overcome these problems, this paper presents an efficient peripheral blood cell image recognition and classification using a combination of the salp swarm algorithm and the cat swarm optimization (SSPSO) algorithm-based optimized convolutional neural networks (SSPSO-CNN) method. This paper uses the CNN approach to classify five peripheral blood cells such as eosinophil, basophil, lymphocytes, monocytes, and neutrophils without any human intervention. The other objective of this paper is to propose an improved version of salp swarm optimizer (SSO) using particle swarm optimization (PSO) to attain competitive classification performance over the database of the blood cell images. In this paper, the CNN uses VGG19 architecture for training purposes. The accuracy of the classification achieved with VGG19 models is 98%. The proposed model based on the CNN approach optimized by SSPSO achieves high classification accuracy and provides automatic peripheral blood cell classification. This method establishes the fine-tuning process to develop a classifier trained using 10 674 images obtained from medical practice. The proposed method augmented the performance in terms of high precision and [Formula: see text]1-score and obtained an overall classification accuracy of 99%.

Leukocyte segmentation in peripheral blood images using a novel edge strength cue-based location detection method

The classification of leukocytes in peripheral blood images is an important milestone to be achieved because it can greatly assist pathologists to diagnose diseases such as leukemia, anemia, and other blood disorders. To a certain extent, a good segmentation method for identifying leukocytes from their background is the first step to the efficient functioning of the leukocytes classification system. However, the morphological structure of leukocytes, poor contrast, and the variations in their shape and size lead to the degradation of the segmentation accuracy. In this paper, we propose a new leukocyte segmentation framework that first locates and then segments leukocytes from peripheral blood images. Here, the locations of the leukocytes are first identified using a novel edge strength cue (ESc), and later, the Grabcut model is deployed to obtain the segmentation of the leukocytes. The novelty lies in the way the location of the leukocytes is detected, and this improves the leukocyte segmentation accuracy. The experimental evaluation is performed on ALL-IDB1, Cellavision, and LISC datasets for leukocyte segmentation based on the detection of the ESc location. Experimental results are evaluated using precision, recall, and F-score measures. The proposed method outperforms the state-of-the-art techniques. Additionally, the computation time of the proposed method is analyzed and presented in the study. Graphical Abstract Leukocytes Location Detection and Segmentation.

A dataset of microscopic peripheral blood cell images for development of automatic recognition systems

This article makes available a dataset that was used for the development of an automatic recognition system of peripheral blood cell images using convolutional neural networks [1]. The dataset contains a total of 17,092 images of individual normal cells, which were acquired using the analyzer CellaVision DM96 in the Core Laboratory at the Hospital Clinic of Barcelona. The dataset is organized in the following eight groups: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts and platelets or thrombocytes. The size of the images is 360 × 363 pixels, in format jpg, and they were annotated by expert clinical pathologists. The images were captured from individuals without infection, hematologic or oncologic disease and free of any pharmacologic treatment at the moment of blood collection.

This high-quality labelled dataset may be used to train and test machine learning and deep learning models to recognize different types of normal peripheral blood cells. To our knowledge, this is the first publicly available set with large numbers of normal peripheral blood cells, so that it is expected to be a canonical dataset for model benchmarking.

Sequential classification system for recognition of malaria infection using peripheral blood cell images

Aims

Morphological recognition of red blood cells infected with malaria parasites is an important task in the laboratory practice. Nowadays, there is a lack of specific automated systems able to differentiate malaria with respect to other red blood cell inclusions. This study aims to develop a machine learning approach able to discriminate parasitised erythrocytes not only from normal, but also from other erythrocyte inclusions, such as Howell-Jolly and Pappenheimer bodies, basophilic stippling as well as platelets overlying red blood cells.

Methods

A total of 15 660 erythrocyte images from 87 smears were segmented using histogram thresholding and watershed techniques, which allowed the extraction of 2852 colour and texture features. Dataset was split into a training and assessment sets. Training set was used to develop the whole system, in which several classification approaches were compared with obtain the most accurate recognition. Afterwards, the recognition system was evaluated with the assessment set, performing two steps: (1) classifying each individual cell image to assess the system’s recognition ability and (2) analysing whole smears to obtain a malaria infection diagnosis.

Results

The selection of the best classification approach resulted in a final sequential system with an accuracy of 97.7% for the six groups of red blood cell inclusions. The ability of the system to detect patients infected with malaria showed a sensitivity and specificity of 100% and 90%, respectively.

Conclusions

The proposed method achieves a high diagnostic performance in the recognition of red blood cell infected with malaria, along with other frequent erythrocyte inclusions.

Recognition of peripheral blood cell images using convolutional neural networks

BACKGROUND AND OBJECTIVES

Morphological analysis is the starting point for the diagnostic approach of more than 80% of hematological diseases. However, the morphological differentiation among different types of normal and abnormal peripheral blood cells is a difficult task that requires experience and skills. Therefore, the paper proposes a system for the automatic classification of eight groups of peripheral blood cells with high accuracy by means of a transfer learning approach using convolutional neural networks. With this new approach, it is not necessary to implement image segmentation, the feature extraction becomes automatic and existing models can be fine-tuned to obtain specific classifiers.

METHODS

A dataset of 17,092 images of eight classes of normal peripheral blood cells was acquired using the CellaVision DM96 analyzer. All images were identified by pathologists as the ground truth to train a model to classify different cell types: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (myelocytes, metamyelocytes and promyelocytes), erythroblasts and platelets. Two designs were performed based on two architectures of convolutional neural networks, Vgg-16 and Inceptionv3. In the first case, the networks were used as feature extractors and these features were used to train a support vector machine classifier. In the second case, the same networks were fine-tuned with our dataset to obtain two end-to-end models for classification of the eight classes of blood cells.

RESULTS

In the first case, the experimental test accuracies obtained were 86% and 90% when extracting features with Vgg-16 and Inceptionv3, respectively. On the other hand, in the fine-tuning experiment, global accuracy values of 96% and 95% were obtained using Vgg-16 and Inceptionv3, respectively. All the models were trained and tested using Keras and Tensorflow with a Nvidia Titan XP Graphics Processing Unit.

CONCLUSIONS

The main contribution of this paper is a classification scheme involving a convolutional neural network trained to discriminate among eight classes of cells circulating in peripheral blood. Starting from a state-of-the-art general architecture, we have established a fine-tuning procedure to develop an end-to-end classifier trained using a dataset with over 17,000 cell images obtained from clinical practice. The performance obtained when testing the system has been truly satisfactory, the values of precision, sensitivity, and specificity being excellent. To summarize, the best overall classification accuracy has been 96.2%.

Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis

AIMS

Morphological differentiation among different blast cell lineages is a difficult task and there is a lack of automated analysers able to recognise these abnormal cells. This study aims to develop a machine learning approach to predict the diagnosis of acute leukaemia using peripheral blood (PB) images.

METHODS

A set of 442 smears was analysed from 206 patients. It was split into a training set with 75% of these smears and a testing set with the remaining 25%. Colour clustering and mathematical morphology were used to segment cell images, which allowed the extraction of 2,867 geometric, colour and texture features. Several classification techniques were studied to obtain the most accurate classification method. Afterwards, the classifier was assessed with the images of the testing set. The final strategy was to predict the patient's diagnosis using the PB smear, and the final assessment was done with the cell images of the smears of the testing set.

RESULTS

The highest classification accuracy was achieved with the selection of 700 features with linear discriminant analysis. The overall classification accuracy for the six groups of cell types was 85.8%, while the overall classification accuracy for individual smears was 94% as compared with the true confirmed diagnosis.

CONCLUSIONS

The proposed method achieves a high diagnostic precision in the recognition of different types of blast cells among other mononuclear cells circulating in blood. It is the first encouraging step towards the idea of being a diagnostic support tool in the future.