Parallel entropic auto-thresholding

In this paper, we examine a multi-level thresholding algorithm based on a number of phases including peak-search, fuzzy logic and entropy of the fuzzy membership function. Analysis of the algorithm is presented to show its properties and behaviours at the various cascaded stages. The fuzzy entropy function of the image histogram is computed using S-function membership and Shannon's entropy function. To establish a suitable fuzzy region bandwidth, we have used a peak-search method based on successive clipping of the image histogram. Location of the valleys in the entropy function correspond to the certainties within the fuzzy region of the image. These certainties are used to indicate an optimal segmentation pattern for multi-level image thresholding. We compare and contrast this method of thresholding with a maximum entropy method. We have implemented the technique in parallel on a transputer-based machine as well as on a cluster of SUN4 workstations, availing ourselves of the PVM communication kernel. A parallel algorithm for the maximum entropy method is given, which significantly reduces computation times. An objective method is used to evaluate the resulting images.