An Optimal Histogram Segmentation Method Using Peak Detection and Its Application to Threshold Selection

Abstract

This paper proposes an optimal histogram segmentation method using peak detection based on the consideration of statistical tests. For all peak detection algorithms proposed so far, results are dependent on the parameters to be specified. The method described, under the hypothesis of a mixture distribution formed by two neighboring peaks, tests whether to combine the peaks into one or keep them intact as two independent distributions according to the maximum location of squared Fisher distance. Test processing continues until no successive peaks can be combined. Peaks detected with different parameters converge to the same number, which can be considered as optimal, as it is verified that each peak can be treated as an independent distribution. In the experiment, the proposed method is applied to several gray-level histograms of images, and its effectiveness is verified by results. The optimal gray levels and thresholds for quantization can thus be estimated.