IMAGE PROCESSING TECHNIQUE FOR THYROID USING IMPROVED HILL CLIMBING ALGORITHM
Abstract
Image segmentation is a fundamental and challenging task in image processing and computer vision. The color image segmentation is attracting more attention due to the color image provides more information than the gray image. In this paper, we propose a variation model based on a convex K-means approach to segment color images. The proposed variation method uses a combination of l1 and l2 regularizes to maintain edge information of objects in images while overcoming the staircase effect. Meanwhile, our onstage strategy is an improved version based on the smoothing and thresholding strategy, which contributes to improving the accuracy of segmentation. Transforming colorful image in to gray image), Image segmentation (thresholding) and feature extraction (hill climbing algorithm) to determine cold thyroid nodules automatically with high accuracy.
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References
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