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A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.

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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.