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Particle Swarm Optimization based Edge Detection Algorithms for Computer Tomography Images
Background/Objectives: Detection of image edges plays an important role in medical image processing, segmentation and computer vision applications. Methods: It is necessary to have a better edge detection algorithm for the diagnosis of the abnormalities in the images and based on the diagnosis, a treatment procedure can be decided. The existing edge detection algorithms like Canny and Sobel are lack in Edge Preservation Factor (EPF) and they pose a low Signal to Noise Ratio (SNR). These algorithms are not good for noisy images. Findings: To overcome these issues, in this paper a Particle Swarm Optimization (PSO) based algorithm is proposed. Improvements/Applications: Experimental result proves that the PSO is better when compared with the existing edge detection techniques.
CT Images, Edge Detection, Particle Swarm Optimization.
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