Detection and characterisation of vessels in retinal images.

Doctor of Philosophy in Mathematics, Statistics & Computer Science. University of KwaZulu-Natal, Durban 2015.As retinopathies such as diabetic retinopathy (DR) and retinopathy of prematurity (ROP) continue to be the major causes of blindness globally, regular retinal examinations of patients can assist in the early detection of the retinopathies. The manual detection of retinal vessels is a very tedious and time consuming task as it requires about two hours to manually detect vessels in each retinal image. Automatic vessel segmentation has been helpful in achieving speed, improved diagnosis and progress monitoring of these diseases but has been challenging due to complexities such as the varying width of the retinal vessels from very large to very small, low contrast of thin vessels with respect to background and noise due to nonhomogeneous illumination in the retinal images. Although several supervised and unsupervised segmentation methods have been proposed in the literature, the segmentation of thinner vessels, connectivity loss of the vessels and time complexity remain the major challenges. In order to address these problems, this research work investigated di erent unsupervised segmentation approaches to be used in the robust detection of large and thin retinal vessels in a timely e cient manner. Firstly, this thesis conducted a study on the use of di erent global thresholding techniques combined with di erent pre-processing and post-processing techniques. Two histogram-based global thresholding techniques namely, Otsu and Isodata were able to detect large retinal vessels but fail to segment the thin vessels because these thin vessels have very low contrast and are di cult to distinguish from the background tissues using the histogram of the retinal images. Two new multi-scale approaches of computing global threshold based on inverse di erence moment and sum-entropy combined with phase congruence are investigated to improve the detection of vessels. One of the findings of this study is that the multi-scale approaches of computing global threshold combined with phase congruence based techniques improved on the detection of large vessels and some of the thin vessels. They, however, failed to maintain the width of the detected vessels. The reduction in the width of the detected large and thin vessels results in low sensitivity rates while relatively good accuracy rates were maintained. Another study on the use of fuzzy c-means and GLCM sum entropy combined on phase congruence for vessel segmentation showed that fuzzy c-means combined with phase congruence achieved a higher average accuracy rates of 0.9431 and 0.9346 but a longer running time of 27.1 seconds when compared with the multi-scale based sum entropy thresholding combined with phase congruence with the average accuracy rates of 0.9416 and 0.9318 with a running time of 10.3 seconds. The longer running time of the fuzzy c-means over the sum entropy thresholding is, however, attributed to the iterative nature of fuzzy c-means. When compared with the literature, both methods achieved considerable faster running time. This thesis investigated two novel local adaptive thresholding techniques for the segmentation of large and thin retinal vessels. The two novel local adaptive thresholding techniques applied two di erent Haralick texture features namely, local homogeneity and energy. Although these two texture features have been applied for supervised image segmentation in the literature, their novelty in this thesis lies in that they are applied using an unsupervised image segmentation approach. Each of these local adaptive thresholding techniques locally applies a multi-scale approach on each of the texture information considering the pixel of interest in relationship with its spacial neighbourhood to compute the local adaptive threshold. The localised multi-scale approach of computing the thresholds handled the challenge of the vessels' width variation. Experiments showed significant improvements in the average accuracy and average sensitivity rates of these techniques when compared with the previously discussed global thresholding methods and state of the art. The two novel local adaptive thresholding techniques achieved a higher reduction of false vessels around the border of the optic disc when compared with some of the previous techniques in the literature. These techniques also achieved a highly improved computational time of 1.9 to 3.9 seconds to segment the vessels in each retinal image when compared with the state of the art. Hence, these two novel local adaptive thresholding techniques are proposed for the segmentation of the vessels in the retinal images. This thesis further investigated the combination of di erence image and kmeans clustering technique for the segmentation of large and thin vessels in retinal images. The pre-processing phase computed a di erence image and k-means clustering technique was used for the vessel detection. While investigating this vessel segmentation method, this thesis established the need for a difference image that preserves the vessel details of the retinal image. Investigating the di erent low pass filters, median filter yielded the best di erence image required by k-means clustering for the segmentation of the retinal vessels. Experiments showed that the median filter based di erence images combined with k-means clustering technique achieved higher average accuracy and average sensitivity rates when compared with the previously discussed global thresholding methods and the state of the art. The median filter based di erence images combined with k-means clustering technique (that is, DIMDF) also achieved a higher reduction of false vessels around the border of the optic disc when compared with some previous techniques in the literature. These methods also achieved a highly improved computational time of 3.4 to 4 seconds when compared with the literature. Hence, the median filter based di erence images combined with k-means clustering technique are proposed for the segmentation of the vessels in retinal images. The characterisation of the detected vessels using tortuosity measure was also investigated in this research. Although several vessel tortuosity methods have been discussed in the literature, there is still need for an improved method that e ciently detects vessel tortuosity. The experimental study conducted in this research showed that the detection of the stationary points helps in detecting the change of direction and twists in the vessels. The combination of the vessel twist frequency obtained using the stationary points and distance metric for the computation of normalised and nonnormalised tortuosity index (TI) measure was investigated. Experimental results showed that the non-normalised TI measure had a stronger correlation with the expert's ground truth when compared with the distance metric and normalised TI measures. Hence, a non-normalised TI measure that combines the vessel twist frequency based on the stationary points and distance metric is proposed for the measurement of vessel tortuosity

Automated Fovea Detection Based on Unsupervised Retinal Vessel Segmentation Method

The Computer Assisted Diagnosis systems could save workloads and give objective diagnostic to ophthalmologists. At first level of automated screening of systems feature extraction is the fundamental step. One of these retinal features is the fovea. The fovea is a small fossa on the fundus, which is represented by a deep-red or red-brown color in color retinal images. By observing retinal images, it appears that the main vessels diverge from the optic nerve head and follow a specific course that can be geometrically modeled as a parabola, with a common vertex inside the optic nerve head and the fovea located along the apex of this parabola curve. Therefore, based on this assumption, the main retinal blood vessels are segmented and fitted to a parabolic model. With respect to the core vascular structure, we can thus detect fovea in the fundus images. For the vessel segmentation, our algorithm addresses the image locally where homogeneity of features is more likely to occur. The algorithm is composed of 4 steps: multi-overlapping windows, local Radon transform, vessel validation, and parabolic fitting. In order to extract blood vessels, sub-vessels should be extracted in local windows. The high contrast between blood vessels and image background in the images cause the vessels to be associated with peaks in the Radon space. The largest vessels, using a high threshold of the Radon transform, determines the main course or overall configuration of the blood vessels which when fitted to a parabola, leads to the future localization of the fovea. In effect, with an accurate fit, the fovea normally lies along the slope joining the vertex and the focus. The darkest region along this line is the indicative of the fovea. To evaluate our method, we used 220 fundus images from a rural database (MUMS-DB) and one public one (DRIVE). The results show that, among 20 images of the first public database (DRIVE) we detected fovea in 85% of them. Also for the MUMS-DB database among 200 images we detect fovea correctly in 83% on them

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

Lesion boundary segmentation using level set methods

This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided by a gradient map built using a combination of histogram equalization and robust statistics. The stopping mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object. We implement the level set using a fast upwind scheme and compare the proposed method against five other segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician marked-up boundaries as ground truth

A GPU-based Evolution Strategy for Optic Disk Detection in Retinal Images

La ejecución paralela de aplicaciones usando unidades de procesamiento gráfico (gpu) ha ganado gran interés en la comunidad académica en los años recientes. La computación paralela puede ser aplicada a las estrategias evolutivas para procesar individuos dentro de una población, sin embargo, las estrategias evolutivas se caracterizan por un significativo consumo de recursos computacionales al resolver problemas de gran tamaño o aquellos que se modelan mediante funciones de aptitud complejas. Este artículo describe la implementación de una estrategia evolutiva para la detección del disco óptico en imágenes de retina usando Compute Unified Device Architecture (cuda). Los resultados experimentales muestran que el tiempo de ejecución para la detección del disco óptico logra una aceleración de 5 a 7 veces, comparado con la ejecución secuencial en una cpu convencional.Parallel processing using graphic processing units (GPUs) has attracted much research interest in recent years. Parallel computation can be applied to evolution strategy (ES) for processing individuals in a population, but evolutionary strategies are time consuming to solve large computational problems or complex fitness functions. In this paper we describe the implementation of an improved ES for optic disk detection in retinal images using the Compute Unified Device Architecture (CUDA) environment. In the experimental results we show that the computational time for optic disk detection task has a speedup factor of 5x and 7x compared to an implementation on a mainstream CPU

A robust lesion boundary segmentation algorithm using level set methods

This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided by a gradient map built using a combination of histogram equalization and robust statistics. The stopping mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object. We compare the proposed method against five other segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician demarcated boundaries as ground truth

Accurate and reliable segmentation of the optic disc in digital fundus images

We describe a complete pipeline for the detection and accurate automatic segmentation of the optic disc in digital fundus images. This procedure provides separation of vascular information and accurate inpainting of vessel-removed images, symmetry-based optic disc localization, and fitting of incrementally complex contour models at increasing resolutions using information related to inpainted images and vessel masks. Validation experiments, performed on a large dataset of images of healthy and pathological eyes, annotated by experts and partially graded with a quality label, demonstrate the good performances of the proposed approach. The method is able to detect the optic disc and trace its contours better than the other systems presented in the literature and tested on the same data. The average error in the obtained contour masks is reasonably close to the interoperator errors and suitable for practical applications. The optic disc segmentation pipeline is currently integrated in a complete software suite for the semiautomatic quantification of retinal vessel properties from fundus camera images (VAMPIRE)

Two Novel Retinal Blood Vessel Segmentation Algorithms

Assessment of blood vessels in retinal images is an important factor for many medical disorders. The changes in the retinal vessels due to the pathologies can be easily identified by segmenting the retinal vessels. Segmentation of retinal vessels is done to identify the early diagnosis of the disease like glaucoma, diabetic retinopathy, macular degeneration, hypertensive retinopathy and arteriosclerosis. In this paper, we propose two automatic blood vessel segmentation methods. The first proposed algorithm starts with the extraction of blood vessel centerline pixels. The final segmentation is obtained using an iterative region growing method that merges the contents of several binary images resulting from vessel width dependent modified morphological filters on normalized retinal images. In the second proposed algorithm the blood vessel is segmented using normalized modified morphological operations and neuro fuzzy classifier. Normalized morphological operations are used to enhance the vessels and neuro fuzzy classifier is used to segment retinal blood vessels. These methods are applied on the publicly available DRIVE database and the experimental results obtained by using green channel images have been presented and their results are compared with recently published methods. The results demonstrate that our algorithms are very effective methods to detect retinal blood vessels.DOI:http://dx.doi.org/10.11591/ijece.v4i3.582

A Novel Retinal Blood Vessel Segmentation Algorithm using Fuzzy segmentation

Assessment of blood vessels in retinal images is an important factor for many medical disorders. The changes in the retinal vessels due to the pathologies can be easily identified by segmenting the retinal vessels. Segmentation of retinal vessels is done to identify the early diagnosis of the disease like glaucoma, diabetic retinopathy, macular degeneration, hypertensive retinopathy and arteriosclerosis. In this paper, we propose an automatic blood vessel segmentation method. The proposed algorithm starts with the extraction of blood vessel centerline pixels. The final segmentation is obtained using an iterative region growing method that merges the binary images resulting from centerline detection part with the image resulting from fuzzy vessel segmentation part. In this proposed algorithm, the blood vessel is enhanced using modified morphological operations and the salt and pepper noises are removed from retinal images using Adaptive Fuzzy Switching Median filter. This method is applied on two publicly available databases, the DRIVE and the STARE and the experimental results obtained by using green channel images have been presented and compared with recently published methods. The results demonstrate that our algorithm is very effective method to detect retinal blood vessels.DOI:http://dx.doi.org/10.11591/ijece.v4i4.625