Detection of blood vessels in human retinal images using Ant Colony Optimisation method
25 Apr 2011-International Journal of Biomedical Engineering and Technology (Inderscience Publishers)-Vol. 5, Iss: 4, pp 360
TL;DR: Results show that the ACO method provides high visual quality output with better detection of blood vessels, and provides better delineation, distinctly differentiates central veins, extracts small blood vessels and detects abnormalities in the image.
Abstract: In this, an attempt has been made to analyse blood vessels in human retinal digital images using Ant Colony Optimisation (ACO) based edge detection algorithm and was then correlated with Otsu and Matched filter methods. Results show that the ACO method provides high visual quality output with better detection of blood vessels. It provides better delineation, distinctly differentiates central veins, extracts small blood vessels and detects abnormalities in the image. The ratio of vessel-to-vessel free area using ACO method is distinctly different for normal and abnormal images ( p < 0.005). It appears that this study is useful for mass screening and avoids complications at later stages of diseases.
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Journal Article•
TL;DR: This work has shown that artificial ants in ACO essentially are randomized construction procedures that generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components.
Abstract: Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species [1]. Artificial ants in ACO essentially are randomized construction procedures that generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components. Since the first ACO algorithm has been proposed in 1991, this algorithmic method has attracted a large number of researchers and in the meantime it has reached a significant level of maturity. In fact, ACO is now a well-established search technique for tackling a wide variety of computationally hard problems.
18 citations
TL;DR: The geometrical views of both retinal artery and vein have been extricated from the blood vessels of the retinal fundus image utilizing morphological operations and the segmented arteries and veins are demonstrated utilizing the impedance-modeling method and Finite Element Analysis.
Abstract: The retinal vasculature has been recognized as a fundamental element in glaucoma as well as diabetic retinopathy. Segmentation of retinal blood vessels is of considerable clinical significance for diagnosing the glaucoma disease at an early stage. With the intention of glaucoma detection, initially, retinal images are acquired by utilizing advanced capture devices for image content. The present investigation has been developed for the detailed computational model analysis of the blood flow in physiologically sensible retinal arterial and venous networks. The geometrical views of both retinal artery and vein have been extricated from the blood vessels of the retinal fundus image utilizing morphological operations. The segmented arteries and veins are demonstrated utilizing the impedance-modeling method and Finite Element Analysis is utilized for the portrayal of arteries and veins to decide the biomechanical parameters of the blood that incorporates structural analysis and computational fluid analysis. The anticipated parameters are classified based on the blood flow attributes by using Support Vector Machine (SVM). The proposed technique accomplishes the maximum accuracy of 94.86% for the proficient prediction of Glaucomatous disease contrasted with existing strategies.
7 citations
TL;DR: A scheme for automated segmentation of FAZ in colour fundus images is proposed and result shows that the proposed scheme has successfully detected the FAZ.
Abstract: One of the diabetes complications, diabetic retinopathy (DR), is characterised by the damage of retinal vessels, especially on the macular region. Located at the centre of retina and appeared as a cloudy dark spot in colour fundus image, macula is a fundamental area for high acumen of colour vision. Foveal avascular zone (FAZ) is located at the centre of macula and encircled by interconnected capillary beds. FAZ has a round or oval shape with an average diameter of 500-600 μm. In DR patients, the FAZ becomes larger due to the loss of perifoveal retinal vessels. In this study, a scheme for automated segmentation of FAZ in colour fundus images is proposed. The scheme consists of four stages: pre-processing, image enhancement, vessels segmentation and FAZ segmentation. Result shows that the average sensitivity, specificity and accuracy obtained are 80.86%, 99.17% and 97.49%. This indicates that the proposed scheme has successfully detected the FAZ.
6 citations
Cites background from "Detection of blood vessels in human..."
...Kavitha and Ramakrishnan (2011) proposed ant colony optimisation (ACO) and edge detection techniques to detect the retinal blood vessels in human retinal images....
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TL;DR: In this work, subcortical regions of autism spectrum disorder are analysed using fuzzy Gaussian distribution model-based distance regularised multi-phase level set method in autistic MR brain images and it is found the segmented autistic subcorts have reduced area and are statistically significant.
Abstract: In this work, subcortical regions of autism spectrum disorder are analysed using fuzzy Gaussian distribution model-based distance regularised multi-phase level set method in autistic MR brain images. The fuzzy Gaussian distribution model is used as the intensity discriminator. The segmented images are validated with the ground truth using geometrical measure area. The results show that the fuzzy Gaussian distribution model-based multi-phase level set method is able to extract the subcortical tissue boundaries. The subcortical regions segmented using this method gives high correlation with ground truth. The corpus callosum area gives very high (R = 0.94) correlation. The brain stem and cerebellum present high correlations of 0.89 and 0.84, respectively. Also, it is found the segmented autistic subcortical regions have reduced area and are statistically significant (p < 0.0001). The ratio metric analysis proves the relation in reduction of the area in subcortical regions with total brain area.
5 citations
Cites background from "Detection of blood vessels in human..."
...Segmentation is achieved in biomedical image applications based on edge, threshold, cluster, region growing and contour methods (Kavitha and Ramakrishnan, 2011; Thamarai and Malmathanraj, 2011; Neeraj et al., 2009; Badredine and Bornia, 2014; Kayalvizhi et al., 2013)....
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19 Mar 2012
TL;DR: The results show that it is possible to differentiate the normal and abnormal retinal images using the features derived using Canny with morphological preprocessing, which is better than the other two methods.
Abstract: In this work, an attempt has been made to analyze retinal images for Content Based Image Retrieval (CBIR) application. Different normal and abnormal images are subjected to vessel detection using Canny based edge detection method with and without preprocessing. Canny segmentation using morphological preprocessing is compared with conventional Canny without preprocessing and contrast stretching based preprocessing method. Essential features are extracted from the segmented images. The similarity matching is carried out between the features obtained from the query image and retinal images stored in the database. The best matched images are ranked and retrieved with appropriate assessment. The results show that it is possible to differentiate the normal and abnormal retinal images using the features derived using Canny with morphological preprocessing. The recall of this CBIR system is found to be 82% using the Canny with morphological preprocessing and is better than the other two methods. It appears that this method is useful to analyze retinal images using CBIR systems.
3 citations
References
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37,017 citations
Book•
01 Jan 2004
TL;DR: Ant colony optimization (ACO) is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals as discussed by the authors In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization.
Abstract: Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony Ant colony optimization exploits a similar mechanism for solving optimization problems From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO The goal of this article is to introduce ant colony optimization and to survey its most notable applications
6,861 citations
TL;DR: A method is presented for automated segmentation of vessels in two-dimensional color images of the retina based on extraction of image ridges, which coincide approximately with vessel centerlines, which is compared with two recently published rule-based methods.
Abstract: A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. and Jiang et al. . The results show that our method is significantly better than the two rule-based methods (p<0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.
3,416 citations
21 Apr 2009
TL;DR: Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species as discussed by the authors.
Abstract: Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species [1]. Artificial ants in ACO essentially are randomized construction procedures that generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components. Since the first ACO algorithm has been proposed in 1991, this algorithmic method has attracted a large number of researchers and in the meantime it has reached a significant level of maturity. In fact, ACO is now a well-established search technique for tackling a wide variety of computationally hard problems.
2,424 citations
TL;DR: An automated method to locate and outline blood vessels in images of the ocular fundus that uses local and global vessel features cooperatively to segment the vessel network is described.
Abstract: Describes an automated method to locate and outline blood vessels in images of the ocular fundus. Such a tool should prove useful to eye care specialists for purposes of patient screening, treatment evaluation, and clinical study. The authors' method differs from previously known methods in that it uses local and global vessel features cooperatively to segment the vessel network. The authors evaluate their method using hand-labeled ground truth segmentations of 20 images. A plot of the operating characteristic shows that the authors' method reduces false positives by as much as 15 times over basic thresholding of a matched filter response (MFR), at up to a 75% true positive rate. For a baseline, they also compared the ground truth against a second hand-labeling, yielding a 90% true positive and a 4% false positive detection rate, on average. These numbers suggest there is still room for a 15% true positive rate improvement, with the same false positive rate, over the authors' method. They are making all their images and hand labelings publicly available for interested researchers to use in evaluating related methods.
2,206 citations