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

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.

[1]  Chia-Ling Tsai,et al.  Automated Model-Based Segmentation, Tracing, and Analysis of Retinal Vasculature from Digital Fundus Images , 2003 .

[2]  Roberto Marcondes Cesar Junior,et al.  Blood vessels segmentation in nonmydriatic images using wavelets and statistical classifiers , 2003, 16th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2003).

[3]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[5]  Ana Maria Mendonça,et al.  Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction , 2006, IEEE Transactions on Medical Imaging.

[6]  S. Poonguzhali,et al.  Evaluation of feature extraction methods for classification of liver abnormalities in ultrasound images , 2007 .

[7]  M. Goldbaum,et al.  Detection of blood vessels in retinal images using two-dimensional matched filters. , 1989, IEEE transactions on medical imaging.

[8]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[9]  Bram van Ginneken,et al.  Comparative study of retinal vessel segmentation methods on a new publicly available database , 2004, SPIE Medical Imaging.

[10]  Herbert F. Jelinek,et al.  Comparison of various methods to delineate blood vessels in retinal images , 2005 .

[11]  Gady Agam,et al.  Probabilistic retinal vessel segmentation , 2007, SPIE Medical Imaging.

[12]  Guoliang Fan,et al.  An efficient algorithm for extraction of anatomical structures in retinal images , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[13]  Peng Huang,et al.  An artificial ant colonies approach to medical image segmentation , 2008, Comput. Methods Programs Biomed..

[14]  Yannis A. Tolias,et al.  A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering , 1998, IEEE Transactions on Medical Imaging.

[15]  Hong Yan,et al.  Blood Vessel Extraction Based on Mumford Shah Model and Skeletonization , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[16]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[17]  A. Pinz,et al.  Mapping the human retina , 1996, IEEE Transactions on Medical Imaging.

[18]  I. Deary,et al.  Retinal image analysis: Concepts, applications and potential , 2006, Progress in Retinal and Eye Research.

[19]  Shengli Xie,et al.  An ant colony optimization algorithm for image edge detection , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[20]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[21]  Phen Chiak See,et al.  Application of ant colony optimisation algorithms in solving facility layout problems formulated as quadratic assignment problems: a review , 2008 .