An Approach to Identify Optic Disc in Human Retinal Images Using Ant Colony Optimization Method

Authors:
Ganesan Kavitha;Swaminathan Ramakrishnan
Affiliations:
Department of Electronics Engineering, MIT Campus, Anna University, Chennai, India 600044;Department of Instrumentation Engineering, Madras Institute of Technology Campus, Anna University, Chennai, India 600044
Venue:
Journal of Medical Systems
Year:
2010

Citing 8
Cited 1

Zero-crossing interval correction in tracing eye-fundus blood vessels

Pattern Recognition
Medical Data Mining and Knowledge Discovery

Medical Data Mining and Knowledge Discovery
Blood Vessels Segmentation in Retina: Preliminary Assessment of the Mathematical Morphology and of the Wavelet Transform Techniques

SIBGRAPI '01 Proceedings of the 14th Brazilian Symposium on Computer Graphics and Image Processing
Image Thresholding Using Ant Colony Optimization

CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Automated Detection of Optic Disc Location in Retinal Images

CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
An artificial ant colonies approach to medical image segmentation

Computer Methods and Programs in Biomedicine
Automatic Tracing of Optic Disc and Exudates from Color Fundus Images Using Fixed and Variable Thresholds

Journal of Medical Systems
Ant colony optimization

IEEE Computational Intelligence Magazine

Combining case-based reasoning with Bee Colony Optimization for dose planning in well differentiated thyroid cancer treatment

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work, an attempt has been made to identify optic disc in retinal images using digital image processing and optimization based edge detection algorithm. The edge detection was carried out using Ant Colony Optimization (ACO) technique with and without pre-processing and was correlated with morphological operations based method. The performance of the pre-processed ACO algorithm was analysed based on visual quality, computation time and its ability to preserve useful edges. The results demonstrate that the ACO method with pre-processing provides high visual quality output with better optic disc identification. Computation time taken for the process was also found to be less. This method preserves nearly 50% more edge pixel distribution when compared to morphological operations based method. In addition to improve optic disc identification, the proposed algorithm also distinctly differentiates between blood vessels and macula in the image. These studies appear to be clinically relevant because automated analyses of retinal images are important for ophthalmological interventions.