Medical image registration in image guided surgery: Issues, challenges and research opportunities

PRSCS-Net: Progressive 3D/2D rigid Registration network with the guidance of Single-view Cycle Synthesis

The 3D/2D registration for 3D pre-operative images (computed tomography, CT) and 2D intra-operative images (X-ray) plays an important role in image-guided spine surgeries. Conventional iterative-based approaches suffer from time-consuming processes. Existing learning-based approaches require high computational costs and face poor performance on large misalignment because of projection-induced losses or ill-posed reconstruction. In this paper, we propose a Progressive 3D/2D rigid Registration network with the guidance of Single-view Cycle Synthesis, named PRSCS-Net. Specifically, we first introduce the differentiable backward/forward projection operator into the single-view cycle synthesis network, which reconstructs corresponding 3D geometry features from two 2D intra-operative view images (one from the input, and the other from the synthesis). In this way, the problem of limited views during reconstruction can be solved. Subsequently, we employ a self-reconstruction path to extract latent representation from pre-operative 3D CT images. The following pose estimation process will be performed in the 3D geometry feature space, which can solve the dimensional gap, greatly reduce the computational complexity, and ensure that the features extracted from pre-operative and intra-operative images are as relevant as possible to pose estimation. Furthermore, to enhance the ability of our model for handling large misalignment, we develop a progressive registration path, including two sub-registration networks, aiming to estimate the pose parameters via two-step warping volume features. Finally, our proposed method has been evaluated on a public dataset CTSpine1k and an in-house dataset C-ArmLSpine for 3D/2D registration. Results demonstrate that PRSCS-Net achieves state-of-the-art registration performance in terms of registration accuracy, robustness, and generalizability compared with existing methods. Thus, PRSCS-Net has potential for clinical spinal disease surgical planning and surgical navigation systems.

Deep Learning-Based Fine-Tuning Approach of Coarse Registration for Ear-Nose-Throat (ENT) Surgical Navigation Systems

Accurate registration between medical images and patient anatomy is crucial for surgical navigation systems in minimally invasive surgeries. This study introduces a novel deep learning-based refinement step to enhance the accuracy of surface registration without disrupting established workflows. The proposed method integrates a machine learning model between conventional coarse registration and ICP fine registration. A deep-learning model was trained using simulated anatomical landmarks with introduced localization errors. The model architecture features global feature-based learning, an iterative prediction structure, and independent processing of rotational and translational components. Validation with silicon-masked head phantoms and CT imaging compared the proposed method to both conventional registration and a recent deep-learning approach. The results demonstrated significant improvements in target registration error (TRE) across different facial regions and depths. The average TRE for the proposed method (1.58 ± 0.52 mm) was significantly lower than that of the conventional (2.37 ± 1.14 mm) and previous deep-learning (2.29 ± 0.95 mm) approaches (p < 0.01). The method showed a consistent performance across various facial regions and enhanced registration accuracy for deeper areas. This advancement could significantly enhance precision and safety in minimally invasive surgical procedures.

Advances of surgical robotics: image-guided classification and application

Surgical robotics application in the field of minimally invasive surgery has developed rapidly and has been attracting increasingly more research attention in recent years. A common consensus has been reached that surgical procedures are to become less traumatic and with the implementation of more intelligence and higher autonomy, which is a serious challenge faced by the environmental sensing capabilities of robotic systems. One of the main sources of environmental information for robots are images, which are the basis of robot vision. In this review article, we divide clinical image into direct and indirect based on the object of information acquisition, and into continuous, intermittent continuous, and discontinuous according to the target-tracking frequency. The characteristics and applications of the existing surgical robots in each category are introduced based on these two dimensions. Our purpose in conducting this review was to analyze, summarize, and discuss the current evidence on the general rules on the application of image technologies for medical purposes. Our analysis gives insight and provides guidance conducive to the development of more advanced surgical robotics systems in the future.

A Rapid Head Organ Localization System Based on Clinically Realistic Images: A 3D Two Step Progressive Registration Method with CVH Anatomical Knowledge Mapping

Rapid localization of ROI (Region of Interest) for tomographic medical images (TMIs) is an important foundation for efficient image reading, computer-aided education, and well-informed rights of patients. However, due to the multimodality of clinical TMIs, the complexity of anatomy, and the deformation of organs caused by diseases, it is difficult to have a universal and low-cost method for ROI organ localization. This article focuses on actual concerns of TMIs from medical students, engineers, interdisciplinary researchers, and patients, exploring a universal registration method between the clinical CT/MRI dataset and CVH (Chinese Visible Human) to locate the organ ROI in a low-cost and lightweight way. The proposed method is called Two-step Progressive Registration (TSPR), where the first registration adopts "eye-nose triangle" features to determine the spatial orientation, and the second registration adopts the circular contour to determine the spatial scale, ultimately achieving CVH anatomical knowledge automated mapping. Through experimentation with representative clinical TMIs, the registration results are capable of labeling the ROI in the images well and can adapt to the deformation problem of ROI, as well as local extremum problems that are prone to occur in inter-subject registration. Unlike the ideal requirements for TMIs' data quality in laboratory research, TSPR has good adaptability to incomplete and non-thin-layer quality in real clinical data in a low-cost and lightweight way. This helps medical students, engineers, and interdisciplinary researchers independently browse images, receive computer-aided education, and provide patients with better access to well-informed services, highlighting the potential of digital public health and medical education.

A Review of Medical Image Registration for Different Modalities

Medical image registration has become pivotal in recent years with the integration of various imaging modalities like X-ray, ultrasound, MRI, and CT scans, enabling comprehensive analysis and diagnosis of biological structures. This paper provides a comprehensive review of registration techniques for medical images, with an in-depth focus on 2D-2D image registration methods. While 3D registration is briefly touched upon, the primary emphasis remains on 2D techniques and their applications. This review covers registration techniques for diverse modalities, including unimodal, multimodal, interpatient, and intra-patient. The paper explores the challenges encountered in medical image registration, including geometric distortion, differences in image properties, outliers, and optimization convergence, and discusses their impact on registration accuracy and reliability. Strategies for addressing these challenges are highlighted, emphasizing the need for continual innovation and refinement of techniques to enhance the accuracy and reliability of medical image registration systems. The paper concludes by emphasizing the importance of accurate medical image registration in improving diagnosis.

Comparing regularized Kelvinlet functions and the finite element method for registration of medical images to sparse organ data

Image-guided surgery collocates patient-specific data with the physical environment to facilitate surgical decision making. Unfortunately, these guidance systems commonly become compromised by intraoperative soft-tissue deformations. Nonrigid image-to-physical registration methods have been proposed to compensate for deformations, but clinical utility requires compatibility of these techniques with data sparsity and temporal constraints in the operating room. While finite element models can be effective in sparse data scenarios, computation time remains a limitation to widespread deployment. This paper proposes a registration algorithm that uses regularized Kelvinlets, which are analytical solutions to linear elasticity in an infinite domain, to overcome these barriers. This algorithm is demonstrated and compared to finite element-based registration on two datasets: a phantom liver deformation dataset and an in vivo breast deformation dataset. The regularized Kelvinlets algorithm resulted in a significant reduction in computation time compared to the finite element method. Accuracy as evaluated by target registration error was comparable between methods. Average target registration errors were 4.6 ± 1.0 and 3.2 ± 0.8 mm on the liver dataset and 5.4 ± 1.4 and 6.4 ± 1.5 mm on the breast dataset for the regularized Kelvinlets and finite element method, respectively. Limitations of regularized Kelvinlets include the lack of organ-specific geometry and the assumptions of linear elasticity and infinitesimal strain. Despite limitations, this work demonstrates the generalizability of regularized Kelvinlets registration on two soft-tissue elastic organs. This method may improve and accelerate registration for image-guided surgery, and it shows the potential of using regularized Kelvinlets on medical imaging data.

A review of synthetic strategy, SAR, docking, simulation studies, and mechanism of action of isoxazole derivatives as anticancer agents

Breast cancer (BC) is a global health concern and the leading cause of cancerous death among women across the world, BC has been characterized by fresh lump in the breast or underarm (armpit), thickened or swollen. Worldwide estimated 9.6 million deaths in 2018-2019. Numerous drugs have been approved by FDA for BC treatment but showed numerous adverse effects like bioavailability issues, selectivity issues, and toxicity issues. Therefore, there is an immediate need to develop new molecules that are non-toxic and more efficient for treating cancer. Isoxazole derivatives have gained popularity over the few years due to their effective antitumor potential. These derivatives work against cancer by inhibiting the thymidylate enzyme, inducing apoptosis, inhibiting tubulin polymerization, protein kinase inhibition, and aromatase inhibition. In this study, we have concentrated on the isoxazole derivative with structure-activity relationship study, various synthesis techniques, mechanism of action, docking, and simulation studies pertaining to BC receptors. Hence the development of isoxazole derivatives with improved therapeutic efficacy will inspire further progress in improving human health.Communicated by Ramaswamy H. Sarma.

Evaluating normalized registration and preprocessing methodologies for the analysis of brain MRI in pediatric patients with shunt-treated hydrocephalus

Introduction

Registration to a standardized template (i.e. "normalization") is a critical step when performing neuroimaging studies. We present a comparative study involving the evaluation of general-purpose registration algorithms for pediatric patients with shunt treated hydrocephalus. Our sample dataset presents a number of intersecting challenges for registration, representing the potentially large deformations to both brain structures and overall brain shape, artifacts from shunts, and morphological differences corresponding to age. The current study assesses the normalization accuracy of shunt-treated hydrocephalus patients using freely available neuroimaging registration tools.

Methods

Anatomical neuroimages from eight pediatric patients with shunt-treated hydrocephalus were normalized. Four non-linear registration algorithms were assessed in addition to the preprocessing steps of skull-stripping and bias-correction. Registration accuracy was assessed using the Dice Coefficient (DC) and Hausdorff Distance (HD) in subcortical and cortical regions.

Results

A total of 592 registrations were performed. On average, normalizations performed using the brain extracted and bias-corrected images had a higher DC and lower HD compared to full head/ non-biased corrected images. The most accurate registration was achieved using SyN by ANTs with skull-stripped and bias corrected images. Without preprocessing, the DARTEL Toolbox was able to produce normalized images with comparable accuracy. The use of a pediatric template as an intermediate registration did not improve normalization.

Discussion

Using structural neuroimages from patients with shunt-treated pediatric hydrocephalus, it was demonstrated that there are tools which perform well after specified pre-processing steps were taken. Overall, these results provide insight to the performance of registration programs that can be used for normalization of brains with complex pathologies.

A deep learning classification task for brain navigation in rodents using micro-Doppler ultrasound imaging

Positioning and navigation are essential components of neuroimaging as they improve the quality and reliability of data acquisition, leading to advances in diagnosis, treatment outcomes, and fundamental understanding of the brain. Functional ultrasound imaging is an emerging technology providing high-resolution images of the brain vasculature, allowing for the monitoring of brain activity. However, as the technology is relatively new, there is no standardized tool for inferring the position in the brain from the vascular images. In this study, we present a deep learning-based framework designed to address this challenge. Our approach uses an image classification task coupled with a regression on the resulting probabilities to determine the position of a single image. To evaluate its performance, we conducted experiments using a dataset of 51 rat brain scans. The training positions were extracted at intervals of 375 μm, resulting in a positioning error of 176 μm. Further GradCAM analysis revealed that the predictions were primarily driven by subcortical vascular structures. Finally, we assessed the robustness of our method in a cortical stroke where the brain vasculature is severely impaired. Remarkably, no specific increase in the number of misclassifications was observed, confirming the method's reliability in challenging conditions. Overall, our framework provides accurate and flexible positioning, not relying on a pre-registered reference but rather on conserved vascular patterns.

Advances in medical image analysis with vision Transformers: A comprehensive review

The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision. Among other merits, Transformers are witnessed as capable of learning long-range dependencies and spatial correlations, which is a clear advantage over convolutional neural networks (CNNs), which have been the de facto standard in Computer Vision problems so far. Thus, Transformers have become an integral part of modern medical image analysis. In this review, we provide an encyclopedic review of the applications of Transformers in medical imaging. Specifically, we present a systematic and thorough review of relevant recent Transformer literature for different medical image analysis tasks, including classification, segmentation, detection, registration, synthesis, and clinical report generation. For each of these applications, we investigate the novelty, strengths and weaknesses of the different proposed strategies and develop taxonomies highlighting key properties and contributions. Further, if applicable, we outline current benchmarks on different datasets. Finally, we summarize key challenges and discuss different future research directions. In addition, we have provided cited papers with their corresponding implementations in https://github.com/mindflow-institue/Awesome-Transformer.

SMILE: Siamese Multi-scale Interactive-representation LEarning for Hierarchical Diffeomorphic Deformable image registration

Deformable medical image registration plays an important role in many clinical applications. It aims to find a dense deformation field to establish point-wise correspondences between a pair of fixed and moving images. Recently, unsupervised deep learning-based registration methods have drawn more and more attention because of fast inference at testing stage. Despite remarkable progress, existing deep learning-based methods suffer from several limitations including: (a) they often overlook the explicit modeling of feature correspondences due to limited receptive fields; (b) the performance on image pairs with large spatial displacements is still limited since the dense deformation field is regressed from features learned by local convolutions; and (c) desirable properties, including topology-preservation and the invertibility of transformation, are often ignored. To address above limitations, we propose a novel Convolutional Neural Network (CNN) consisting of a Siamese Multi-scale Interactive-representation LEarning (SMILE) encoder and a Hierarchical Diffeomorphic Deformation (HDD) decoder. Specifically, the SMILE encoder aims for effective feature representation learning and spatial correspondence establishing while the HDD decoder seeks to regress the dense deformation field in a coarse-to-fine manner. We additionally propose a novel Local Invertible Loss (LIL) to encourage topology-preservation and local invertibility of the regressed transformation while keeping high registration accuracy. Extensive experiments conducted on two publicly available brain image datasets demonstrate the superiority of our method over the state-of-the-art (SOTA) approaches. Specifically, on the Neurite-OASIS dataset, our method achieved an average DSC of 0.815 and an average ASSD of 0.633 mm.

Feasibility of tracked ultrasound registration for pelvic-abdominal tumor navigation: a patient study

PURPOSE

Surgical navigation techniques can guide surgeons in localizing pelvic-abdominal malignancies. For abdominal navigation, accurate patient registration is crucial and is generally performed using an intra-operative cone-beam CT (CBCT). However, this method causes 15-min surgical preparation workflow interruption and radiation exposure, and more importantly, it cannot be repeated during surgery to compensate for large patient movement. As an alternative, the accuracy and feasibility of tracked ultrasound (US) registration are assessed in this patient study.

METHODS

Patients scheduled for surgical navigation during laparotomy of pelvic-abdominal malignancies were prospectively included. In the operating room, two percutaneous tracked US scans of the pelvic bone were acquired: one in supine and one in Trendelenburg patient position. Postoperatively, the bone surface was semiautomatically segmented from US images and registered to the bone surface on the preoperative CT scan. The US registration accuracy was computed using the CBCT registration as a reference and acquisition times were compared. Additionally, both US measurements were compared to quantify the registration error caused by patient movement into Trendelenburg.

RESULTS

In total, 18 patients were included and analyzed. US registration resulted in a mean surface registration error of 1.2 ± 0.2 mm and a mean target registration error of 3.3 ± 1.4 mm. US acquisitions were 4 × faster than the CBCT scans (two-sample t-test P < 0.05) and could even be performed during standard patient preparation before skin incision. Patient repositioning in Trendelenburg caused a mean target registration error of 7.7 ± 3.3 mm, mainly in cranial direction.

CONCLUSION

US registration based on the pelvic bone is accurate, fast and feasible for surgical navigation. Further optimization of the bone segmentation algorithm will allow for real-time registration in the clinical workflow. In the end, this would allow intra-operative US registration to correct for large patient movement.

TRIAL REGISTRATION

This study is registered in ClinicalTrials.gov (NCT05637359).

A two-step deep learning method for 3DCT-2DUS kidney registration during breathing

This work proposed KidneyRegNet, a novel deep registration pipeline for 3D CT and 2D U/S kidney scans of free breathing, which comprises a feature network, and a 3D-2D CNN-based registration network. The feature network has handcrafted texture feature layers to reduce the semantic gap. The registration network is an encoder-decoder structure with loss of feature-image-motion (FIM), which enables hierarchical regression at decoder layers and avoids multiple network concatenation. It was first pretrained with a retrospective dataset cum training data generation strategy and then adapted to specific patient data under unsupervised one-cycle transfer learning in onsite applications. The experiment was performed on 132 U/S sequences, 39 multiple-phase CT and 210 public single-phase CT images, and 25 pairs of CT and U/S sequences. This resulted in a mean contour distance (MCD) of 0.94 mm between kidneys on CT and U/S images and MCD of 1.15 mm on CT and reference CT images. Datasets with small transformations resulted in MCDs of 0.82 and 1.02 mm, respectively. Large transformations resulted in MCDs of 1.10 and 1.28 mm, respectively. This work addressed difficulties in 3DCT-2DUS kidney registration during free breathing via novel network structures and training strategies.

Transformers in medical imaging: A survey

Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as de facto operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues. Specifically, we survey the use of Transformers in medical image segmentation, detection, classification, restoration, synthesis, registration, clinical report generation, and other tasks. In particular, for each of these applications, we develop taxonomy, identify application-specific challenges as well as provide insights to solve them, and highlight recent trends. Further, we provide a critical discussion of the field's current state as a whole, including the identification of key challenges, open problems, and outlining promising future directions. We hope this survey will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of Transformer models in medical imaging. Finally, to cope with the rapid development in this field, we intend to regularly update the relevant latest papers and their open-source implementations at https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging.

Automatic registration of dental CT and 3D scanned model using deep split jaw and surface curvature

BACKGROUND AND OBJECTIVES

In the medical field, various image registration applications have been studied. In dentistry, the registration of computed tomography (CT) volume data and 3D optically scanned models is essential for various clinical applications, including orthognathic surgery, implant surgical planning, and augmented reality. Our purpose was to present a fully automatic registration method of dental CT data and 3D scanned models.

METHODS

We use a 2D convolutional neural network to regress a curve splitting the maxilla (i.e., upper jaw) and mandible (i.e., lower jaw) and the points specifying the front and back ends of the crown from the CT data. Using this regressed information, we extract the point cloud and vertices corresponding to the tooth crown from the CT and scanned data, respectively. We introduce a novel metric, called curvature variance of neighbor (CVN), to discriminate between highly fluctuating and smoothly varying regions of the tooth crown. The registration based on CVN enables more accurate fine registration while reducing the effects of metal artifacts. Moreover, the proposed method does not require any preprocessing such as extracting the iso-surface for the tooth crown from the CT data, thereby significantly reducing the computation time.

RESULTS

We evaluated the proposed method with the comparison to several promising registration techniques. Our experimental results using three datasets demonstrated that the proposed method exhibited higher registration accuracy (i.e., 2.85, 1.92, and 7.73 times smaller distance errors for individual datasets) and smaller computation time (i.e., 4.12 times faster registration) than one of the state-of-the-art methods. Moreover, the proposed method worked considerably well for partially scanned data, whereas other methods suffered from the unbalancing of information between the CT and scanned data.

CONCLUSIONS

The proposed method was able to perform fully automatic and highly accurate registration of dental CT data and 3D scanned models, even with severe metal artifacts. In addition, it could achieve fast registration because it did not require any preprocessing for iso-surface reconstruction from the CT data.

Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation

PURPOSE

This study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable registration for abdominal imaging.

METHODS

Different training strategies, loss functions, and transfer learning schemes were considered. Furthermore, an augmentation layer which generates artificial training image pairs on-the-fly was proposed, in addition to a loss layer that enables dynamic loss weighting.

RESULTS

Guiding registration using segmentations in the training step proved beneficial for deep-learning-based image registration. Finetuning the pretrained model from the brain MRI dataset to the abdominal CT dataset further improved performance on the latter application, removing the need for a large dataset to yield satisfactory performance. Dynamic loss weighting also marginally improved performance, all without impacting inference runtime.

CONCLUSION

Using simple concepts, we improved the performance of a commonly used deep image registration architecture, VoxelMorph. In future work, our framework, DDMR, should be validated on different datasets to further assess its value.

Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data

The biopsy is a gold standard method for tumor grading. However, due to its invasive nature, it has sometimes proved fatal for brain tumor patients. As a result, a non-invasive computer-aided diagnosis (CAD) tool is required. Recently, many magnetic resonance imaging (MRI)-based CAD tools have been proposed for brain tumor grading. The MRI has several sequences, which can express tumor structure in different ways. However, a suitable MRI sequence for brain tumor classification is not yet known. The most common brain tumor is 'glioma', which is the most fatal form. Therefore, in the proposed study, to maximize the classification ability between low-grade versus high-grade glioma, three datasets were designed comprising three MRI sequences: T1-Weighted (T1W), T2-weighted (T2W), and fluid-attenuated inversion recovery (FLAIR). Further, five well-established convolutional neural networks, AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 were adopted for tumor classification. An ensemble algorithm was proposed using the majority vote of above five deep learning (DL) models to produce more consistent and improved results than any individual model. Five-fold cross validation (K5-CV) protocol was adopted for training and testing. For the proposed ensembled classifier with K5-CV, the highest test accuracies of 98.88 ± 0.63%, 97.98 ± 0.86%, and 94.75 ± 0.61% were achieved for FLAIR, T2W, and T1W-MRI data, respectively. FLAIR-MRI data was found to be most significant for brain tumor classification, where it showed a 4.17% and 0.91% improvement in accuracy against the T1W-MRI and T2W-MRI sequence data, respectively. The proposed ensembled algorithm (MajVot) showed significant improvements in the average accuracy of three datasets of 3.60%, 2.84%, 1.64%, 4.27%, and 1.14%, respectively, against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50.

Federate learning on Web browsing data with statically and machine learning technique

Purpose

Federation analytics approaches are a present area of study that has already progressed beyond the analysis of metrics and counts. It is possible to acquire aggregated information about on-device data by training machine learning models using federated learning techniques without any of the raw data ever having to leave the devices in the issue. Web browser forensics research has been focused on individual Web browsers or architectural analysis of specific log files rather than on broad topics. This paper aims to propose major tools used for Web browser analysis.

Design/methodology/approach

Each kind of Web browser has its own unique set of features. This allows the user to choose their preferred browsers or to check out many browsers at once. If a forensic examiner has access to just one Web browser's log files, he/she makes it difficult to determine which sites a person has visited. The agent must thus be capable of analyzing all currently available Web browsers on a single workstation and doing an integrated study of various Web browsers.

Findings

Federated learning has emerged as a training paradigm in such settings. Web browser forensics research in general has focused on certain browsers or the computational modeling of specific log files. Internet users engage in a wide range of activities using an internet browser, such as searching for information and sending e-mails.

Originality/value

It is also essential that the investigator have access to user activity when conducting an inquiry. This data, which may be used to assess information retrieval activities, is very critical. In this paper, the authors purposed a major tool used for Web browser analysis. This study's proposed algorithm is capable of protecting data privacy effectively in real-world experiments.

Nonfinite-modality data augmentation for brain image registration

Brain image registration is fundamental for brain medical image analysis. However, the lack of paired images with diverse modalities and corresponding ground truth deformations for training hinder its development. We propose a novel nonfinite-modality data augmentation for brain image registration to combat this. Specifically, some available whole-brain segmentation masks, including complete fine brain anatomical structures, are collected from the actual brain dataset, OASIS-3. One whole-brain segmentation mask can generate many nonfinite-modality brain images by randomly merging some fine anatomical structures and subsequently sampling the intensities for each fine anatomical structure using random Gaussian distribution. Furthermore, to get more realistic deformations as the ground truth, an improved 3D Variational Auto-encoder (VAE) is proposed by introducing the intensity-level reconstruction loss and the structure-level reconstruction loss. Based on the generated images and trained improved 3D VAE, a new Synthetic Nonfinite-Modality Brain Image Dataset (SNMBID) is created. Experiments show that pre-training on SNMBID can improve the accuracy of registration. Notably, SNMBID can be a landmark for evaluating other brain registration methods, and the model trained on the SNMBID can be a baseline for the brain image registration task. Our code is available at https://github.com/MangoWAY/SMIBID_BrainRegistration.

Federate learning of corporate social authority and industry 4.0 that focus on young people: a strategic management framework for human resources

Purpose

The young population of the globe is defined by individuals aged 15 to 24 years. Based on statistics from the Instituto Brasileiro de Geografia e Estatística (IBGE), the second largest women population among 15 years as well as 19 years was in 2017 only behind 35 and 39 years. At this time, the Brazilian male population was higher. The difficulties of the young generation affected the preceding generation and promoted social dynamism. The worldwide data shows that the generation of young and the digital world have been constantly sought, but in reality, approximately one-third of the population in 2017 had no access to the internet.

Design/methodology/approach

The worldwide movement around topics such as strategy on its threefold basis and Industry 4.0 enable a link to company duty towards society to be established. This present study was produced from 1 March 2020 to 2 September 2020 via resources of human and literature evaluation relating to the idea of strategic, Industry 4.0, the responsibility of society and the creation of youth. Its motive is the global creation of youth. Two recommendations should be made after studying the literature and information gathering that enabled “analyzing social responsibility of the company and industry 4.0 with a pivot on young creation: a strategic framework for resources of human management”.

Findings

The adoption of defensible practices and technology bring forth by the revolution in industrial is emphasized worldwide.

Originality/value

The focus on the usage of these ideas is essential, so that young people can absorb the workforce in the labour market. To achieve this, the CSR idea combines this theoretical triple-created recent study.

Augmented reality navigation with ultrasound-assisted point cloud registration for percutaneous ablation of liver tumors

PURPOSE

We present a novel augmented reality (AR) surgical navigation method with ultrasound-assisted point cloud registration for percutaneous ablation of liver tumors. A preliminary study is carried out to verify its feasibility.

METHODS

Two three-dimensional (3D) point clouds of the liver surface are derived from the preoperative images and intraoperative tracked US images, respectively. To compensate for the soft tissue deformation, the point cloud registration between the preoperative images and the liver is performed using the non-rigid iterative closest point (ICP) algorithm. A 3D AR device based on integral videography technology is designed to accurately display naked-eye 3D images for surgical navigation. Based on the above registration, naked-eye 3D images of the liver surface, planning path, entry points, and tumor can be overlaid in situ through our 3D AR device. Finally, the AR-guided targeting accuracy is evaluated through entry point positioning.

RESULTS

Experiments on both the liver phantom and in vitro pork liver were conducted. Several entry points on the liver surface were used to evaluate the targeting accuracy. The preliminary validation on the liver phantom showed average entry-point errors (EPEs) of 2.34 ± 0.45 mm, 2.25 ± 0.72 mm, 2.71 ± 0.82 mm, and 2.50 ± 1.11 mm at distinct US point cloud coverage rates of 100%, 75%, 50%, and 25%, respectively. The average EPEs of the deformed pork liver were 4.49 ± 1.88 mm and 5.02 ± 2.03 mm at the coverage rates of 100% and 75%, and the average covered-entry-point errors (CEPEs) were 4.96 ± 2.05 mm and 2.97 ± 1.37 mm at 50% and 25%, respectively.

CONCLUSION

Experimental outcomes demonstrate that the proposed AR navigation method based on US-assisted point cloud registration has achieved an acceptable targeting accuracy on the liver surface even in the case of liver deformation.

Reattachable fiducial skin marker for automatic multimodality registration

Purpose

Fusing image information has become increasingly important for optimal diagnosis and treatment of the patient. Despite intensive research towards markerless registration approaches, fiducial marker-based methods remain the default choice for a wide range of applications in clinical practice. However, as especially non-invasive markers cannot be positioned reproducibly in the same pose on the patient, pre-interventional imaging has to be performed immediately before the intervention for fiducial marker-based registrations.

Methods

We propose a new non-invasive, reattachable fiducial skin marker concept for multi-modal registration approaches including the use of electromagnetic or optical tracking technologies. We furthermore describe a robust, automatic fiducial marker localization algorithm for computed tomography (CT) and magnetic resonance imaging (MRI) images. Localization of the new fiducial marker has been assessed for different marker configurations using both CT and MRI. Furthermore, we applied the marker in an abdominal phantom study. For this, we attached the marker at three poses to the phantom, registered ten segmented targets of the phantom’s CT image to live ultrasound images and determined the target registration error (TRE) for each target and each marker pose.

Results

Reattachment of the marker was possible with a mean precision of 0.02 mm ± 0.01 mm. Our algorithm successfully localized the marker automatically in all ($$n=201$$ n = 201 ) evaluated CT/MRI images. Depending on the marker pose, the mean ($$n=10$$ n = 10 ) TRE of the abdominal phantom study ranged from 1.51 ± 0.75 mm to 4.65 ± 1.22 mm.

Conclusions

The non-invasive, reattachable skin marker concept allows reproducible positioning of the marker and automatic localization in different imaging modalities. The low TREs indicate the potential applicability of the marker concept for clinical interventions, such as the puncture of abdominal lesions, where current image-based registration approaches still lack robustness and existing marker-based methods are often impractical.

2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation

Image-guided surgery (IGS) can reduce the risk of tissue damage and improve the accuracy and targeting of lesions by increasing the surgery’s visual field. Three-dimensional (3D) medical images can provide spatial location information to determine the location of lesions and plan the operation process. For real-time tracking and adjusting the spatial position of surgical instruments, two-dimensional (2D) images provide real-time intraoperative information. In this experiment, 2D/3D medical image registration algorithm based on the gray level is studied, and the registration based on normalized cross-correlation is realized. The Gaussian Laplacian second-order differential operator is introduced as a new similarity measure to increase edge information and internal detail information to solve single information and small convergence regions of the normalized cross-correlation algorithm. The multiresolution strategy improves the registration accuracy and efficiency to solve the low efficiency of the normalized cross-correlation algorithm.

Breast image registration for surgery: Insights on material mechanics modeling

Breast conserving surgery (BCS) is a common procedure for early-stage breast cancer patients. Supine preoperative magnetic resonance (MR) breast imaging for visualizing tumor location and extent, while not standard for procedural guidance, more closely represents the surgical presentation compared to conventional diagnostic pendant positioning. Optimal utilization for surgical guidance, however, requires a fast and accurate image-to-physical registration from preoperative imaging to intraoperative surgical presentation. In this study, three registration methods were investigated on healthy volunteers' breasts (n=11) with the arm-down position simulating preoperative imaging and arm-up position simulating intraoperative data. The registration methods included: (1) point-based rigid registration using synthetic fiducials, (2) non-rigid biomechanical model-based registration using sparse data, and (3) a data-dense 3D diffeomorphic image-based registration from the Advanced Normalization Tools (ANTs) repository. The average target registration errors (TRE) were 10.4 ± 2.3, 6.4 ± 1.5, and 2.8 ± 1.3 mm (mean ± standard deviation) and the average fiducial registration errors (FRE) were 7.8 ± 1.7, 2.5 ± 1.1, and 3.1 ± 1.1 mm (mean ± standard deviation) for the point-based rigid, nonrigid biomechanical, and ANTs registrations, respectively. Additionally, common mechanics-based deformation metrics (volume change and anisotropy) were calculated from the ANTs deformation field. The average metrics revealed anisotropic tissue behavior and a statistical difference in volume change between glandular and adipose tissue, suggesting that nonrigid modeling methods may be improved by incorporating material heterogeneity and anisotropy. Overall, registration accuracy significantly improved with increasingly flexible registration methods, which may inform future development of image guidance systems for lumpectomy procedures.

A Medical Image Registration Method Based on Progressive Images

Background

Medical image registration is an essential task for medical image analysis in various applications. In this work, we develop a coarse-to-fine medical image registration method based on progressive images and SURF algorithm (PI-SURF) for higher registration accuracy.

Methods

As a first step, the reference image and the floating image are fused to generate multiple progressive images. Thereafter, the floating image and progressive image are registered to get the coarse registration result based on the SURF algorithm. For further improvement, the coarse registration result and the reference image are registered to perform fine image registration. The appropriate progressive image has been investigated by experiments. The mutual information (MI), normal mutual information (NMI), normalized correlation coefficient (NCC), and mean square difference (MSD) similarity metrics are used to demonstrate the potential of the PI-SURF method.

Results

For the unimodal and multimodal registration, the PI-SURF method achieves the best results compared with the mutual information method, Demons method, Demons+B-spline method, and SURF method. The MI, NMI, and NCC of PI-SURF are improved by 15.5%, 1.31%, and 7.3%, respectively, while MSD decreased by 13.2% for the multimodal registration compared with the optimal result of the state-of-the-art methods.

Conclusions

The extensive experiments show that the proposed PI-SURF method achieves higher quality of registration.

Multimodality-based knee joint modelling method with bone and cartilage structures for total knee arthroplasty

OBJECTIVE

We propose a robust and accurate knee joint modelling method with bone and cartilage structures to enable accurate surgical guidance for knee surgery.

METHODS

A multimodality registration strategy is proposed to fuse magnetic resonance (MR) and computed tomography (CT) images of the femur and tibia separately to remove spatial inconsistency caused by knee bending in CT/MR scans. Automatic segmentation of the femur, tibia and cartilages is carried out with region of interest clustering and intensity analysis based on the multimodal fusion of images.

RESULTS

Experimental results show that the registration error is 1.13 ± 0.30 mm. The Dice similarity coefficient values of the proposed segmentation method of the femur, tibia, femoral and tibial cartilages are 0.969, 0.966, 0.910 and 0.872, respectively.

CONCLUSIONS

This study demonstrates the feasibility and effectiveness of multimodality-based registration and segmentation methods for knee joint modelling. The proposed method can provide users with 3D anatomical models of the femur, tibia, and cartilages with few human inputs.

Construction of Vertebral Body Tracking Algorithm Based on Dynamic Imaging Parameter Measurement and Its Application in the Treatment of Lumbar Instability

Static imaging measurements could not truly reflect the dynamic panorama of the lumbar movement process, and the abnormal activities between the lumbar vertebrae and their dynamic balance could not be observed, resulting in difficulties in the mechanism analysis of lumbar instability and the efficacy evaluation of manipulation therapy. Therefore, this paper constructed a vertebral tracking algorithm based on dynamic imaging parameter measurement through imaging parameter measurement and calculation. According to the imaging data obtained by vertebral body tracking algorithm, the corresponding statistical methods were used to compare the functional scores before and after manipulation and the changes of imaging data, so as to evaluate the therapeutic effect of manipulation on lumbar instability. Through the clinical observation and imaging analysis of 15 patients with lumbar instability before and after manipulation treatment, it is verified that the vertebra tracking algorithm is effective in the vertebra tracking and plays a positive role in the treatment of lumbar instability.

A Comparative Study of Automatic Localization Algorithms for Spherical Markers within 3D MRI Data

Localization of features and structures in images is an important task in medical image-processing. Characteristic structures and features are used in diagnostics and surgery planning for spatial adjustments of the volumetric data, including image registration or localization of bone-anchors and fiducials. Since this task is highly recurrent, a fast, reliable and automated approach without human interaction and parameter adjustment is of high interest. In this paper we propose and compare four image processing pipelines, including algorithms for automatic detection and localization of spherical features within 3D MRI data. We developed a convolution based method as well as algorithms based on connected-components labeling and analysis and the circular Hough-transform. A blob detection related approach, analyzing the Hessian determinant, was examined. Furthermore, we introduce a novel spherical MRI-marker design. In combination with the proposed algorithms and pipelines, this allows the detection and spatial localization, including the direction, of fiducials and bone-anchors.

Microscope integrated optical coherence tomography system combined with augmented reality

One of the disadvantages in microscope-integrated optical coherence tomography (MI-OCT) systems is that medical images acquired via different modalities are usually displayed independently. Hence, surgeons have to match two-dimensional and three-dimensional images of the same operative region subjectively. In this paper, we propose a simple registration method to overcome this problem by using guided laser points. This method combines augmented reality with an existing MI-OCT system. The basis of our idea is to introduce a guiding laser into the system, which allows us to identify fiducials in microscopic images. At first, the applied voltages of the scanning galvanometer mirror are used to calculate the fiducials' coordinates in an OCT model. After gathering data at the corresponding points' coordinates, the homography matrix and camera parameters are used to superimpose a reconstructed model on microscopic images. After performing experiments with artificial and animal eyes, we successfully obtain two-dimensional microscopic images of scanning regions with depth information. Moreover, the registration error is 0.04 mm, which is within the limits of medical and surgical errors. Our proposed method could have many potential applications in ophthalmic procedures.

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Data extraction and visualization of 3D medical images of ocular blood vessels are performed by geometric transformation algorithm, which first performs random resonance response in a global sense to achieve detection of high-contrast coarse blood vessels and then redefines the input signal as a local image shielding the global detection result to achieve enhanced detection of low-contrast microfine vessels and complete multilevel random resonance segmentation detection. Finally, a random resonance detection method for fundus vessels based on scale decomposition is proposed, in which the images are scale decomposed, the high-frequency signals containing detailed information are randomly resonantly enhanced to achieve microfine vessel segmentation detection, and the final vessel segmentation detection results are obtained after fusing the low-frequency image signals. The optimal stochastic resonance response of the nonlinear model of neurons in the global sense is obtained to detect the high-grade intensity signal; then, the input signal is defined as a local image with high-contrast blood vessels removed, and the parameters are optimized before the detection of the low-grade intensity signal. Finally, the multilevel random resonance response is fused to obtain the segmentation results of the fundus retinal vessels. The sensitivity of the multilevel segmentation method proposed in this paper is significantly improved compared with the global random resonance results, indicating that the method proposed in this paper has obvious advantages in the segmentation of vessels with low-intensity levels. The image library was tested, and the experimental results showed that the new method has a better segmentation effect on low-contrast microscopic blood vessels. The new method not only makes full use of the noise for weak signal detection and segmentation but also provides a new idea of how to achieve multilevel segmentation and recognition of medical images.

Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration

Histopathologic assessment routinely provides rich microscopic information about tissue structure and disease process. However, the sections used are very thin, and essentially capture only 2D representations of a certain tissue sample. Accurate and robust alignment of sequentially cut 2D slices should contribute to more comprehensive assessment accounting for surrounding 3D information. Towards this end, we here propose a two-step diffeomorphic registration approach that aligns differently stained histology slides to each other, starting with an initial affine step followed by estimating a deformation field. It was quantitatively evaluated on ample (n = 481) and diverse data from the automatic non-rigid histological image registration challenge, where it was awarded the second rank. The obtained results demonstrate the ability of the proposed approach to robustly (average robustness = 0.9898) and accurately (average relative target registration error = 0.2%) align differently stained histology slices of various anatomical sites while maintaining reasonable computational efficiency (<1 min per registration). The method was developed by adapting a general-purpose registration algorithm designed for 3D radiographic scans and achieved consistently accurate results for aligning high-resolution 2D histologic images. Accurate alignment of histologic images can contribute to a better understanding of the spatial arrangement and growth patterns of cells, vessels, matrix, nerves, and immune cell interactions.

Automatic Registration Between Dental Cone-Beam CT and Scanned Surface via Deep Pose Regression Neural Networks and Clustered Similarities

Computerized registration between maxillofacial cone-beam computed tomography (CT) images and a scanned dental model is an essential prerequisite for surgical planning for dental implants or orthognathic surgery. We propose a novel method that performs fully automatic registration between a cone-beam CT image and an optically scanned model. To build a robust and automatic initial registration method, deep pose regression neural networks are applied in a reduced domain (i.e., two-dimensional image). Subsequently, fine registration is performed using optimal clusters. A majority voting system achieves globally optimal transformations while each cluster attempts to optimize local transformation parameters. The coherency of clusters determines their candidacy for the optimal cluster set. The outlying regions in the iso-surface are effectively removed based on the consensus among the optimal clusters. The accuracy of registration is evaluated based on the Euclidean distance of 10 landmarks on a scanned model, which have been annotated by experts in the field. The experiments show that the registration accuracy of the proposed method, measured based on the landmark distance, outperforms the best performing existing method by 33.09%. In addition to achieving high accuracy, our proposed method neither requires human interactions nor priors (e.g., iso-surface extraction). The primary significance of our study is twofold: 1) the employment of lightweight neural networks, which indicates the applicability of neural networks in extracting pose cues that can be easily obtained and 2) the introduction of an optimal cluster-based registration method that can avoid metal artifacts during the matching procedures.

Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images

In this study, we propose a fast and accurate method to automatically localize anatomical landmarks in medical images. We employ a global-to-local localization approach using fully convolutional neural networks (FCNNs). First, a global FCNN localizes multiple landmarks through the analysis of image patches, performing regression and classification simultaneously. In regression, displacement vectors pointing from the center of image patches towards landmark locations are determined. In classification, presence of landmarks of interest in the patch is established. Global landmark locations are obtained by averaging the predicted displacement vectors, where the contribution of each displacement vector is weighted by the posterior classification probability of the patch that it is pointing from. Subsequently, for each landmark localized with global localization, local analysis is performed. Specialized FCNNs refine the global landmark locations by analyzing local sub-images in a similar manner, i.e. by performing regression and classification simultaneously and combining the results. Evaluation was performed through localization of 8 anatomical landmarks in CCTA scans, 2 landmarks in olfactory MR scans, and 19 landmarks in cephalometric X-rays. We demonstrate that the method performs similarly to a second observer and is able to localize landmarks in a diverse set of medical images, differing in image modality, image dimensionality, and anatomical coverage.

A Parallel Image Registration Algorithm Based on a Lattice Boltzmann Model

Image registration is a key pre-procedure for high level image processing. However, taking into consideration the complexity and accuracy of the algorithm, the image registration algorithm always has high time complexity. To speed up the registration algorithm, parallel computation is a relevant strategy. Parallelizing the algorithm by implementing Lattice Boltzmann method (LBM) seems a good candidate. In consequence, this paper proposes a novel parallel LBM based model (LB model) for image registration. The main idea of our method consists in simulating the convection diffusion equation through a LB model with an ad hoc collision term. By applying our method on computed tomography angiography images (CTA images), Magnet Resonance images (MR images), natural scene image and artificial images, our model proves to be faster than classical methods and achieves accurate registration. In the continuity of 2D image registration model, the LB model is extended to 3D volume registration providing excellent results in domain such as medical imaging. Our method can run on massively parallel architectures, ranging from embedded field programmable gate arrays (FPGAs) and digital signal processors (DSPs) up to graphics processing units (GPUs).

Recent Trends, Technical Concepts and Components of Computer-Assisted Orthopedic Surgery Systems: A Comprehensive Review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.

Robots and Tools for Remodeling Bone

The field of robotic surgery has progressed from small teams of researchers repurposing industrial robots, to a competitive and highly innovative subsection of the medical device industry. Surgical robots allow surgeons to perform tasks with greater ease, accuracy, or safety, and fall under one of four levels of autonomy; active, semi-active, passive, and remote manipulator. The increased accuracy afforded by surgical robots has allowed for cementless hip arthroplasty, improved postoperative alignment following knee arthroplasty, and reduced duration of intraoperative fluoroscopy among other benefits. Cutting of bone has historically used tools such as hand saws and drills, with other elaborate cutting tools now used routinely to remodel bone. Improvements in cutting accuracy and additional options for safety and monitoring during surgery give robotic surgeries some advantages over conventional techniques. This article aims to provide an overview of current robots and tools with a common target tissue of bone, proposes a new process for defining the level of autonomy for a surgical robot, and examines future directions in robotic surgery.

Sequential Factorized Autoencoder for Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms

OBJECTIVE

This work presents a novel approach to handle the inter-subject variations existing in the population analysis of ECG, applied for localizing the origin of ventricular tachycardia (VT) from 12-lead electrocardiograms (ECGs).

METHODS

The presented method involves a factor disentangling sequential autoencoder (f-SAE) - realized in both long short-term memory (LSTM) and gated recurrent unit (GRU) networks - to learn to disentangle the inter-subject variations from the factor relating to the location of origin of VT. To perform such disentanglement, a pair-wise contrastive loss is introduced.

RESULTS

The presented methods are evaluated on ECG dataset with 1012 distinct pacing sites collected from scar-related VT patients during routine pace-mapping procedures. Experiments demonstrate that, for classifying the origin of VT into the predefined segments, the presented f-SAE improves the classification accuracy by 8.94% from using prescribed QRS features, by 1.5% from the supervised deep CNN network, and 5.15% from the standard SAE without factor disentanglement. Similarly, when predicting the coordinates of the VT origin, the presented f-SAE improves the performance by 2.25 mm from using prescribed QRS features, by 1.18 mm from the supervised deep CNN network and 1.6 mm from the standard SAE.

CONCLUSION

These results demonstrate the importance as well as the feasibility of the presented f-SAE approach for separating inter-subject variations when using 12-lead ECG to localize the origin of VT.

SIGNIFICANCE

This work suggests the important research direction to deal with the well-known challenge posed by inter-subject variations during population analysis from ECG signals.

Optical and Electromagnetic Tracking Systems for Biomedical Applications: A Critical Review on Potentialities and Limitations

Optical and electromagnetic tracking systems represent the two main technologies integrated into commercially-available surgical navigators for computer-assisted image-guided surgery so far. Optical Tracking Systems (OTSs) work within the optical spectrum to track the position and orientation, i.e., pose of target surgical instruments. OTSs are characterized by high accuracy and robustness to environmental conditions. The main limitation of OTSs is the need of a direct line-of-sight between the optical markers and the camera sensor, rigidly fixed into the operating theatre. Electromagnetic Tracking Systems (EMTSs) use electromagnetic field generator to detect the pose of electromagnetic sensors. EMTSs do not require such a direct line-of-sight, however the presence of metal or ferromagnetic sources in the operating workspace can significantly affect the measurement accuracy. The aim of the proposed review is to provide a complete and detailed overview of optical and electromagnetic tracking systems, including working principles, source of error and validation protocols. Moreover, commercial and research-oriented solutions, as well as clinical applications, are described for both technologies. Finally, a critical comparative analysis of the state of the art which highlights the potentialities and the limitations of each tracking system for a medical use is provided.

Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms

PURPOSE

In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms.

METHODS

A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details.

RESULTS

The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations.

CONCLUSION

In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.