The Experts below are selected from a list of 8223 Experts worldwide ranked by ideXlab platform
Emanuele Trucco - One of the best experts on this subject based on the ideXlab platform.
-
a review of machine learning methods for retinal blood Vessel Segmentation and artery vein classification
Medical Image Analysis, 2021Co-Authors: Muthu Rama Krishnan Mookiah, Stephen Hogg, Tom Macgillivray, Vijayaraghavan Prathiba, Rajendra Pradeepa, Viswanathan Mohan, Ranjit Mohan Anjana, Alex S F Doney, Colin N A Palmer, Emanuele TruccoAbstract:The eye affords a unique opportunity to inspect a rich part of the human microvasculature non-invasively via retinal imaging. Retinal blood Vessel Segmentation and classification are prime steps for the diagnosis and risk assessment of microvascular and systemic diseases. A high volume of techniques based on deep learning have been published in recent years. In this context, we review 158 papers published between 2012 and 2020, focussing on methods based on machine and deep learning (DL) for automatic Vessel Segmentation and classification for fundus camera images. We divide the methods into various classes by task (Segmentation or artery-vein classification), technique (supervised or unsupervised, deep and non-deep learning, hand-crafted methods) and more specific algorithms (e.g. multiscale, morphology). We discuss advantages and limitations, and include tables summarising results at-a-glance. Finally, we attempt to assess the quantitative merit of DL methods in terms of accuracy improvement compared to other methods. The results allow us to offer our views on the outlook for Vessel Segmentation and classification for fundus camera images.
-
Leveraging Multiscale Hessian-Based Enhancement With a Novel Exudate Inpainting Technique for Retinal Vessel Segmentation
IEEE Journal of Biomedical and Health Informatics, 2016Co-Authors: Roberto Annunziata, Andrea Garzelli, Lucia Ballerini, Alessandro Mecocci, Emanuele TruccoAbstract:Accurate Vessel detection in retinal images is an important and difficult task. Detection is made more challenging in pathological images with the presence of exudates and other abnormalities. In this paper, we present a new unsupervised Vessel Segmentation approach to address this problem. A novel inpainting filter, called neighborhood estimator before filling, is proposed to inpaint exudates in a way that nearby false positives are significantly reduced during Vessel enhancement. Retinal vascular enhancement is achieved with a multiple-scale Hessian approach. Experimental results show that the proposed Vessel Segmentation method outperforms state-of-the-art algorithms reported in the recent literature, both visually and in terms of quantitative measurements, with overall mean accuracy of 95.62% on the STARE dataset and 95.81% on the HRF dataset.
-
blood Vessel Segmentation and width estimation in ultra wide field scanning laser ophthalmoscopy
Biomedical Optics Express, 2014Co-Authors: Enrico Pellegrini, Tom Macgillivray, Emanuele Trucco, Gavin Robertson, Carmen Alina Lupascu, Jano Van Hemert, Michelle C Williams, David E Newby, Edwin J R Van Beek, Graeme J HoustonAbstract:Features of the retinal vasculature, such as Vessel widths, are considered biomarkers for systemic disease. The aim of this work is to present a supervised approach to Vessel Segmentation in ultra-wide field of view scanning laser ophthalmoscope (UWFoV SLO) images and to evaluate its performance in terms of Segmentation and Vessel width estimation accuracy. The results of the proposed method are compared with ground truth measurements from human observers and with existing state-of-the-art techniques developed for fundus camera images that we optimized for UWFoV SLO images. Our algorithm is based on multi-scale matched filters, a neural network classifier and hysteresis thresholding. After spline-based refinement of the detected Vessel contours, the Vessel widths are estimated from the binary maps. Such analysis is performed on SLO images for the first time. The proposed method achieves the best results, both in Vessel Segmentation and in width estimation, in comparison to other automatic techniques.
-
fabc retinal Vessel Segmentation using adaboost
International Conference of the IEEE Engineering in Medicine and Biology Society, 2010Co-Authors: Carmen Alina Lupascu, Domenico Tegolo, Emanuele TruccoAbstract:This paper presents a method for automated Vessel Segmentation in retinal images. For each pixel in the field of view of the image, a 41-D feature vector is constructed, encoding information on the local intensity structure, spatial properties, and geometry at multiple scales. An AdaBoost classifier is trained on 789 914 gold standard examples of Vessel and nonVessel pixels, then used for classifying previously unseen images. The algorithm was tested on the public digital retinal images for Vessel extraction (DRIVE) set, frequently used in the literature and consisting of 40 manually labeled images with gold standard. Results were compared experimentally with those of eight algorithms as well as the additional manual Segmentation provided by DRIVE. Training was conducted confined to the dedicated training set from the DRIVE database, and feature-based AdaBoost classifier (FABC) was tested on the 20 images from the test set. FABC achieved an area under the receiver operating characteristic (ROC) curve of 0.9561, in line with state-of-the-art approaches, but outperforming their accuracy (0.9597 versus 0.9473 for the nearest performer).
Jie Yang - One of the best experts on this subject based on the ideXlab platform.
-
patch based fully convolutional neural network with skip connections for retinal blood Vessel Segmentation
International Conference on Image Processing, 2017Co-Authors: Zhongwei Feng, Jie Yang, Lixiu YaoAbstract:Automated Segmentation of retinal blood Vessels plays an important role in the computer aided diagnosis of retinal diseases. The paper presents a new formulation of patch-based fully Convolutional Neural Networks (CNNs) that allows accurate Segmentation of the retinal blood Vessels. A major modification in this retinal blood Vessel Segmentation task is to improve and speed-up the patch-based fully CNN training by local entropy sampling and a skip CNN architecture with class-balancing loss. The proposed method is experimented on DRIVE dataset and achieves strong performance and significantly outperforms the-state-of-the-art for retinal blood Vessel Segmentation with 78.11% sensitivity, 98.39% specificity, 95.60% accuracy, 87.36% precision and 97.92% AUC score respectively.
-
improving dense conditional random field for retinal Vessel Segmentation by discriminative feature learning and thin Vessel enhancement
Computer Methods and Programs in Biomedicine, 2017Co-Authors: Lei Zhou, Jie YangAbstract:Abstract Background and objectives As retinal Vessels in color fundus images are thin and elongated structures, standard pairwise based random fields, which always suffer the “shrinking bias” problem, are not competent for such Segmentation task. Recently, a dense conditional random field (CRF) model has been successfully used in retinal Vessel Segmentation. Its corresponding energy function is formulated as a linear combination of several unary features and a pairwise term. However, the hand-crafted unary features can be suboptimal in terms of linear models. Here we propose to learn discriminative unary features and enhance thin Vessels for pairwise potentials to further improve the Segmentation performance. Methods Our proposed method comprises four main steps: firstly, image preprocessing is applied to eliminate the strong edges around the field of view (FOV) and normalize the luminosity and contrast inside FOV; secondly, a convolutional neural network (CNN) is properly trained to generate discriminative features for linear models; thirdly, a combo of filters are applied to enhance thin Vessels, reducing the intensity difference between thin and wide Vessels; fourthly, by taking the discriminative features for unary potentials and the thin-Vessel enhanced image for pairwise potentials, we adopt the dense CRF model to achieve the final retinal Vessel Segmentation. The Segmentation performance is evaluated on four public datasets (i.e. DRIVE, STARE, CHASEDB1 and HRF). Results Experimental results show that our proposed method improves the performance of the dense CRF model and outperforms other methods when evaluated in terms of F1-score, Matthews correlation coefficient (MCC) and G-mean, three effective metrics for the evaluation of imbalanced binary classification. Specifically, the F1-score, MCC and G-mean are 0.7942, 0.7656, 0.8835 for the DRIVE dataset respectively; 0.8017, 0.7830, 0.8859 for STARE respectively; 0.7644, 0.7398, 0.8579 for CHASEDB1 respectively; and 0.7627, 0.7402, 0.8812 for HRF respectively. Conclusions The discriminative features learned in CNNs are more effective than hand-crafted ones. Our proposed method performs well in retinal Vessel Segmentation. The architecture of our method is trainable and can be integrated into computer-aided diagnostic (CAD) systems in the future.
-
Three-dimensional Vessel Segmentation using a novel combinatory filter framework.
Physics in Medicine and Biology, 2014Co-Authors: Y Ding, Wil O. C. Ward, T Wästerlid, Penny A. Gowland, Andrew Peters, Jie YangAbstract:Blood Vessel Segmentation is of great importance in medical diagnostic applications. Filter based methods that make use of Hessian matrices have been found to be very useful for blood Vessel Segmentation in both 2D and 3D medical images. However, these methods often fail on images that contain high density microVessels and background noise. The errors in the form of missing, undesired broken or incorrectly merged Vessels eventually lead to poor Segmentation results. In this paper, we present a novel method for 3D Vessel Segmentation that is also suitable for segmenting microVessels, incorporating the advantages of a line filter and a Hessian-based Vessel filter to overcome the problems. The proposed method is shown to be reliable for noisy and inhomogeneous images. Vessels can also be separated based on their scale/thickness so that the method can be used for different medical applications. Furthermore, a quantitative Vessel analysis method based on the multifractal analysis is performed on the segmented vasculature and fractal properties are found in all images.
Sara Moccia - One of the best experts on this subject based on the ideXlab platform.
-
blood Vessel Segmentation algorithms review of methods datasets and evaluation metrics
Computer Methods and Programs in Biomedicine, 2018Co-Authors: Sara Moccia, Elena De Momi, Sara El Hadji, Leonardo S MattosAbstract:Abstract Background Blood Vessel Segmentation is a topic of high interest in medical image analysis since the analysis of Vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic Vessel Segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the Segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and Vessel contrast). Objective This paper aims at reviewing the most recent and innovative blood Vessel Segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood Vessel Segmentation including machine learning, deformable model, and tracking-based approaches. Methods This paper analyzes more than 100 articles focused on blood Vessel Segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted. Discussion Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the Segmentation of pathological Vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy Vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new Vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. Conclusion No single Segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the Vessel Segmentation algorithms so that the most suitable methods can be chosen according to the specific task.
Tom Macgillivray - One of the best experts on this subject based on the ideXlab platform.
-
a review of machine learning methods for retinal blood Vessel Segmentation and artery vein classification
Medical Image Analysis, 2021Co-Authors: Muthu Rama Krishnan Mookiah, Stephen Hogg, Tom Macgillivray, Vijayaraghavan Prathiba, Rajendra Pradeepa, Viswanathan Mohan, Ranjit Mohan Anjana, Alex S F Doney, Colin N A Palmer, Emanuele TruccoAbstract:The eye affords a unique opportunity to inspect a rich part of the human microvasculature non-invasively via retinal imaging. Retinal blood Vessel Segmentation and classification are prime steps for the diagnosis and risk assessment of microvascular and systemic diseases. A high volume of techniques based on deep learning have been published in recent years. In this context, we review 158 papers published between 2012 and 2020, focussing on methods based on machine and deep learning (DL) for automatic Vessel Segmentation and classification for fundus camera images. We divide the methods into various classes by task (Segmentation or artery-vein classification), technique (supervised or unsupervised, deep and non-deep learning, hand-crafted methods) and more specific algorithms (e.g. multiscale, morphology). We discuss advantages and limitations, and include tables summarising results at-a-glance. Finally, we attempt to assess the quantitative merit of DL methods in terms of accuracy improvement compared to other methods. The results allow us to offer our views on the outlook for Vessel Segmentation and classification for fundus camera images.
-
blood Vessel Segmentation and width estimation in ultra wide field scanning laser ophthalmoscopy
Biomedical Optics Express, 2014Co-Authors: Enrico Pellegrini, Tom Macgillivray, Emanuele Trucco, Gavin Robertson, Carmen Alina Lupascu, Jano Van Hemert, Michelle C Williams, David E Newby, Edwin J R Van Beek, Graeme J HoustonAbstract:Features of the retinal vasculature, such as Vessel widths, are considered biomarkers for systemic disease. The aim of this work is to present a supervised approach to Vessel Segmentation in ultra-wide field of view scanning laser ophthalmoscope (UWFoV SLO) images and to evaluate its performance in terms of Segmentation and Vessel width estimation accuracy. The results of the proposed method are compared with ground truth measurements from human observers and with existing state-of-the-art techniques developed for fundus camera images that we optimized for UWFoV SLO images. Our algorithm is based on multi-scale matched filters, a neural network classifier and hysteresis thresholding. After spline-based refinement of the detected Vessel contours, the Vessel widths are estimated from the binary maps. Such analysis is performed on SLO images for the first time. The proposed method achieves the best results, both in Vessel Segmentation and in width estimation, in comparison to other automatic techniques.
Muhammad Moazam Fraz - One of the best experts on this subject based on the ideXlab platform.
-
optimizing the trainable b cosfire filter for retinal blood Vessel Segmentation
PeerJ, 2018Co-Authors: Sufian A Badawi, Muhammad Moazam FrazAbstract:Segmentation of the retinal blood Vessels using filtering techniques is a widely used step in the development of an automated system for diagnostic retinal image analysis. This paper optimized the blood Vessel Segmentation, by extending the trainable B-COSFIRE filter via identification of more optimal parameters. The filter parameters are introduced using an optimization procedure to three public datasets (STARE, DRIVE, and CHASE-DB1). The suggested approach considers analyzing thresholding parameters selection followed by application of background artifacts removal techniques. The approach results are better than the other state of the art methods used for Vessel Segmentation. ANOVA analysis technique is also used to identify the most significant parameters that are impacting the performance results (p-value i 0.05). The proposed enhancement has improved the Vessel Segmentation accuracy in DRIVE, STARE and CHASE-DB1 to 95.47, 95.30 and 95.30, respectively.
-
blood Vessel Segmentation methodologies in retinal images a survey
Computer Methods and Programs in Biomedicine, 2012Co-Authors: Muhammad Moazam Fraz, Paolo Remagnino, Andreas Hoppe, Bunyarit Uyyanonvara, Alicja R Rudnicka, Christopher G Owen, Sarah BarmanAbstract:Retinal Vessel Segmentation algorithms are a fundamental component of automatic retinal disease screening systems. This work examines the blood Vessel Segmentation methodologies in two dimensional retinal images acquired from a fundus camera and a survey of techniques is presented. The aim of this paper is to review, analyze and categorize the retinal Vessel extraction algorithms, techniques and methodologies, giving a brief description, highlighting the key points and the performance measures. We intend to give the reader a framework for the existing research; to introduce the range of retinal Vessel Segmentation algorithms; to discuss the current trends and future directions and summarize the open problems. The performance of algorithms is compared and analyzed on two publicly available databases (DRIVE and STARE) of retinal images using a number of measures which include accuracy, true positive rate, false positive rate, sensitivity, specificity and area under receiver operating characteristic (ROC) curve.
