Image Segmentation Using the Multiphase Level Set in Multiple Color Spaces

The goal of image segmentation in imaging science is to solve the problem of partitioning an image into smaller disjoint homogeneous regions that share similar attributes. The novel technique of the multiphase level set based on principal component analysis (PCA) with adaptively selecting dominant factors for color image segmentation in color spaces is studied here. And simultaneously, the final segmentation is completed by a simple labeling scheme. Then the comparative study of the refined Chan-Vese method is done in multiple color spaces. The experimental results illustrate that the multiphase Chan-Vese algorithm with or without PCA has good segmentation results with fine adaptability in RGB, CIE XYZ, NTSC and YCbCr color spaces where the results of test image changes little. Nevertheless, the h1h2h3 color space, produce poor segmentation on the reliability and accuracy of a set of test images by performance analysis with evaluation indicators.