You are in:Home/Publications/Asad AH, Azar AT, Hassanien AE (2014). A New Heuristic Function of Ant Colony System for Retinal Vessel Segmentation. International Journal of Rough Sets and Data Analysis, 1(2): 15-30.

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Asad AH, Azar AT, Hassanien AE (2014). A New Heuristic Function of Ant Colony System for Retinal Vessel Segmentation. International Journal of Rough Sets and Data Analysis, 1(2): 15-30.
Authors: Not Available
Year: 2014
Keywords: Not Available
Journal: Not Available
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link:
Full paper Ahmad Taher Azar_a-new-heuristic-function-of-ant-colony-system-for-retinal-vessel-segmentation.pdf
Supplementary materials Not Available
Abstract:

The automatic segmentation of blood vessels in retinal images is the crucial stage in any retina diagnosis systems. This article discussed the impact of two improvements to the previous baseline approach for automatic segmentation of retinal blood vessels based on the ant colony system. The first improvement is in features where the length of previous features vector used in segmentation is reduced to the half since four less significant features are replaced by a new more significant feature when applying the correlation-based feature selection heuristic. The second improvement is in ant colony system where a new probability-based heuristic function is applied instead of the previous Euclidean distance based heuristic function. Experimental results showed the improved approach gives better performance than baseline approach when it is tested on DRIVE database of retinal images. Also, the statistical analysis demonstrated that was no statistically significant difference between the baseline and improved approaches in the sensitivity (0.7388± 0.0511 vs. 0.7501±0.0385, respectively; P = 0.4335). On the other hand, statistically significant improvements were found between the baseline and improved approaches for specificity and accuracy (P = 0.0024 and 0.0053, respectively). It was noted that the improved approach showed an increase of 1.1% in the accuracy after applying the new probability-based heuristic function.

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