A fast automatic optimal threshold selection technique for image segmentation

In this article, a fast context-sensitive threshold selection technique is presented to solve the image segmentation problems. In lieu of histogram, the proposed technique employs recently defined energy curve of the image. First, the initial thresholds are selected in the middle of two consecutive peaks on the energy curve. Then based on the cluster validity measure, the optimal number of potential thresholds and the bounds where the optimal value of each potential threshold may exist are determined. Finally, genetic algorithm (GA) is employed to detect the optimal value of each potential threshold from their respective defined bounds. The proposed technique incorporates spatial contextual information of the image in threshold selection process without loosing the benefits of histogram-based techniques. Computationally it is very efficient. Moreover, it is able to determine the optimal number of segments in the input image. To assess the effectiveness of the proposed technique, the results obtained are compared with four state-of-the-art methods cited in the literature. Experimental results on large number of images confirmed the effectiveness of the proposed technique.