Color Image Segmentation by Multilevel Thresholding using a Two Stage Optimization Approach and Fusion
Rafika Harrabi, Ezzedine Ben Braiek · 2014
In this paper, we propose a new color image segmentation method based on a multilevel thresholding algorithm and data fusion techniques. We have revised the Otsu method for selecting optimal threshold values for both unimodal and bimodal distributions, and tested the performance of the new automatic thresholding method called the TSMO (Two-Stage Multi-level Thresholding) on the color images segmentation. This algorithm is iterative and outperforms Otsu's method by greatly reducing the iterations required for computing the between-class variance in an image. For segmentation, we proceed in two steps. In the first step, we begin by identifying the optimal threshold of the tristimuli (R, G and B). In the second step, segmentation results for the three color components are integrated through the fusion rule, in order to get a final reliable and accurate segmentation result. Experimental segmentation results on medical and textured color images demonstrate the value of combing the thresholding technique and fusion rule for color image segmentation. The obtained results show the robustness of the proposed method.