Optimized Multilevel Threshold Selection Using Evolutionary Computing

Thresholding is the method used for segmenting an image to isolate regions of interest from the image. The result of segmentation mainly depends on the selection of proper threshold values and number of classes. This paper proposes a method for optimal selection of threshold values using Evolutionary computing. The proposed method decomposes the given image to reduce its size so that it can be processed faster using Genetic Algorithm. The resultant image is finally mapped onto the original image space. The efficiency of the proposed method is compared with the other multilevel thresholding techniques namely GA-Otsu and GA-Kapur with and without wavelets. From the experimental results, it is inferred that the proposed method takes less time for processing and provides better results compared to existing methods.