| Summary: | Genetic algorithms (GAs) are intended to look for the optimum solution by eliminating the gene strings with the worst fitness. Hence, this paper proposes an optimized edge detection technique based on a genetic algorithm. A training dataset that consists of simple images and their corresponding optimal edge features is employed to obtain the optimum filter coefficients along with the optimum thresholding algorithm. Qualitative and quantitative performance analyses are investigated based on several well-known metrics. The performance of the proposed genetic algorithm-based cost minimization technique is compared to the classical edge detection techniques, fractional-order edge detection filters, and threshold-optimized fractional-order filters. As an application for the proposed algorithm, a strategy to detect the edges of the brain tumour from a patient's MR scan image of the brain is proposed. First, Balance Contrast Enhancement Technique (BCET) is applied to improve the image features to provide better characteristics of medical images. Then the proposed GA edge detection method is employed, with the appropriate training dataset, to detect the fine edges. A comparative analysis is performed on the number of MR scan images as well. The study indicates that the proposed GA edge detection method performs well compared to both classical and fractional-order edge detection methods.
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