Parallel Genetic Algorithm Based Thresholding for Image Segmentation

Threshold plays a vital role in classification of objects and background in a given scene and hence segmentation. Determination of optimal threshold is hard for images exhibiting overlapping histogram distributions. In this paper, we propose a novel strategy of determining the threshold from histogram distributions. A feature image is generated from the given image and the optimal threshold is determined using the histogram of the featured pixels. The featured pixels are generated by considering a fixed window around a pixel. The histogram distributions are discrete in nature and hence Genetic Algorithm (GA) and Parallel Genetic Algorithm (PGA) based clustering algorithms are proposed to determine the optimal thresholds for two and three class problems. The optimal thresholds, thus determined could segment the noisy image. The efficacy of the proposed scheme is compared with that of the Otsu’s approach. Results obtained by the proposed scheme was comparable to that Otsu’s and in some noisy cases our method could be better than the latter one. Satisfactory results could also be obtained even for histograms with overlapping class distributions