Retinal blood vessel segmentation: methods and implementations

Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic or computer-aided diagnosis systems. This thesis, therefore, has investigated different works of image segmentation algorithms and techniques, including unsupervised and supervised methods. Further, the thesis has developed and implemented two systems of the accurate retinal vessel segmentation. The methodologies explained and analyzed in this thesis, have been selected as the most efficient approaches to achieve higher precision, better robustness, and faster execution speed, to meet the strict standard of the modern medical imaging. Based on the intensive investigation and experiments, this thesis has proposed two outstanding implementations of the retinal blood vessel segmentation. The first implementation focuses on the fast, accurate and robust extraction of the retinal vessels using unsupervised techniques, by applying morphology-based global thresholding to draw the retinal venule structure and centerline detection to extract the capillaries. Besides, this system has been designed to minimize the computing complexity and to process multiple independent procedures in parallel. The second proposed system has especially focused on robustness and accuracy in regardless of execution time. This method has utilized the full convolutional neural network trained from a pre-trained semantic segmentation model, which is also called the transfer deep learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition. Both of the implementations have outperformed their related works and have presented a remarkable scientific value for future computer-aided diagnosis applications. What’s more, this thesis is also a research guide which provide readers with the comprehensive knowledge on how to research on the task of retinal vessel segmentation

Retinal blood vessel segmentation: methods and implementations

Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic or computer-aided diagnosis systems. This thesis, therefore, has investigated different works of image segmentation algorithms and techniques, including unsupervised and supervised methods. Further, the thesis has developed and implemented two systems of the accurate retinal vessel segmentation. The methodologies explained and analyzed in this thesis, have been selected as the most efficient approaches to achieve higher precision, better robustness, and faster execution speed, to meet the strict standard of the modern medical imaging. Based on the intensive investigation and experiments, this thesis has proposed two outstanding implementations of the retinal blood vessel segmentation. The first implementation focuses on the fast, accurate and robust extraction of the retinal vessels using unsupervised techniques, by applying morphology-based global thresholding to draw the retinal venule structure and centerline detection to extract the capillaries. Besides, this system has been designed to minimize the computing complexity and to process multiple independent procedures in parallel. The second proposed system has especially focused on robustness and accuracy in regardless of execution time. This method has utilized the full convolutional neural network trained from a pre-trained semantic segmentation model, which is also called the transfer deep learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition. Both of the implementations have outperformed their related works and have presented a remarkable scientific value for future computer-aided diagnosis applications. What’s more, this thesis is also a research guide which provide readers with the comprehensive knowledge on how to research on the task of retinal vessel segmentation

Retinal blood vessel segmentation: methods and implementations

Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic or computer-aided diagnosis systems. This thesis, therefore, has investigated different works of image segmentation algorithms and techniques, including unsupervised and supervised methods. Further, the thesis has developed and implemented two systems of the accurate retinal vessel segmentation. The methodologies explained and analyzed in this thesis, have been selected as the most efficient approaches to achieve higher precision, better robustness, and faster execution speed, to meet the strict standard of the modern medical imaging. Based on the intensive investigation and experiments, this thesis has proposed two outstanding implementations of the retinal blood vessel segmentation. The first implementation focuses on the fast, accurate and robust extraction of the retinal vessels using unsupervised techniques, by applying morphology-based global thresholding to draw the retinal venule structure and centerline detection to extract the capillaries. Besides, this system has been designed to minimize the computing complexity and to process multiple independent procedures in parallel. The second proposed system has especially focused on robustness and accuracy in regardless of execution time. This method has utilized the full convolutional neural network trained from a pre-trained semantic segmentation model, which is also called the transfer deep learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition. Both of the implementations have outperformed their related works and have presented a remarkable scientific value for future computer-aided diagnosis applications. What’s more, this thesis is also a research guide which provide readers with the comprehensive knowledge on how to research on the task of retinal vessel segmentation

Trainable COSFIRE filters for vessel delineation with application to retinal images

Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis. We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding. The results that we achieve on three publicly available data sets (DRIVE: Se = 0.7655, Sp = 0.9704; STARE: Se = 0.7716, Sp = 0.9701; CHASE_DB1: Se = 0.7585, Sp = 0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.peer-reviewe

Structure analysis and lesion detection from retinal fundus images

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Ocular pathology is one of the main health problems worldwide. The number of people with retinopathy symptoms has increased considerably in recent years. Early adequate treatment has demonstrated to be effective to avoid the loss of the vision. The analysis of fundus images is a non intrusive option for periodical retinal screening. Different models designed for the analysis of retinal images are based on supervised methods, which require of hand labelled images and processing time as part of the training stage. On the other hand most of the methods have been designed under the basis of specific characteristics of the retinal images (e.g. field of view, resolution). This compromises its performance to a reduce group of retinal image with similar features. For these reasons an unsupervised model for the analysis of retinal image is required, a model that can work without human supervision or interaction. And that is able to perform on retinal images with different characteristics. In this research, we have worked on the development of this type of model. The system locates the eye structures (e.g. optic disc and blood vessels) as first step. Later, these structures are masked out from the retinal image in order to create a clear field to perform the lesion detection. We have selected the Graph Cut technique as a base to design the retinal structures segmentation methods. This selection allows incorporating prior knowledge to constraint the searching for the optimal segmentation. Different link weight assignments were formulated in order to attend the specific needs of the retinal structures (e.g. shape). This research project has put to work together the fields of image processing and ophthalmology to create a novel system that contribute significantly to the state of the art in medical image analysis. This new knowledge provides a new alternative to address the analysis of medical images and opens a new panorama for researchers exploring this research area.Mexican National Council of Science and Technolog

Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy

Diabetic retinopathy (DR) refers to a complication of diabetes and a prime cause of vision loss in middle-aged people. A timely screening and diagnosis process can reduce the risk of blindness. Fundus imaging is mainly preferred in the clinical analysis of DR. However; the raw fundus images are usually subjected to artifacts, noise, low and varied contrast, which is very hard to process by human visual systems and automated systems. In the existing literature, many solutions are given to enhance the fundus image. However, such approaches are particular and limited to a specific objective that cannot address multiple fundus images. This paper has presented an on-demand preprocessing frame work that integrates different techniques to address geometrical issues, random noises, and comprehensive contrast enhancement solutions. The performance of each preprocessing process is evaluated against peak signal-to-noise ratio (PSNR), and brightness is quantified in the enhanced image. The motive of this paper is to offer a flexible approach of preprocessing mechanism that can meet image enhancement needs based on different preprocessing requirements to improve the quality of fundus imaging towards early-stage diabetic retinopathy identification

AUTOMATIC DETECTION OF DIABETIC RETINOPATHY THROUGH OPTIC DISC USING MORPHOLOGICAL METHODS

This paper proposes a method for the automatic detection of optic disc in retinal images. In the diagnosis and grading, the essential step is recognition of optic disk for diabetic retinopathy. The analysis of directional cross section profile focused on the local maximum pixel of pre-processed image is realized by the proposed method using optic disc detection. Each profile is implemented by peak detection and property like shape, size and height of the peak are estimated. The statistical measure of the estimated values for the attributes, where the orientation of the cross-section changes the constitute feature used in morphological classification to exclude encourages candidates. The result is to find the patient is affected by diabetics or not.Keywords: Diabetic retinopathy, Optic disk, Naives Bayes algorithm, Local maximum region