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. Copyright © 2011 Inderscience Enterprises Ltd.

Swaminathan Ramakrishnan

Department of Applied Mechanics

ACO METHOD

ANT COLONY OPTIMISATION

Digital image

EDGE DETECTION ALGORITHMS

OTSU METHOD

RETINAL IMAGE

RETINAL IMAGES

VISUAL QUALITIES

Algorithms

Artificial Intelligence

blood

edge detection

MATCHED FILTERS

OPHTHALMOLOGY

Optimization

Blood vessels

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Detection of blood vessels in human retinal images using Ant Colony Optimisation method

International Journal of Biomedical Engineering and Technology

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. © 2014 Inderscience Enterprises Ltd.

Segmentation and morphometric analysis of subcortical regions in autistic MR brain images using fuzzy Gaussian distribution model-based distance regularised multi-phase level set

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. © 2012 Springer-Verlag.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Content based human retinal image retrieval using vascular feature extraction

Journal	International Journal of Biomedical Engineering and Technology
ISSN	17526418
Open Access	No