Hybrid threshold optimization between global image and local regions in image segmentation for melasma severity assessment

Abstract

Melasma image segmentation plays a fundamental role for computerized melasma severity assessment. A method of hybrid threshold optimization between a given image and its local regions is proposed and used for melasma image segmentation. An analytic optimal hybrid threshold solution is obtained by minimizing the deviation between the given image and its segmented outcome. This optimal hybrid threshold comprises both local and global information around image pixels and is used to develop an optimal hybrid thresholding segmentation method. The developed method is firstly evaluated based on synthetic images and subsequently used for melasma segmentation and severity assessment. Statistical evaluations of experimental results based on real-world melasma images show that the proposed method outperforms other state-of-the-art thresholding segmentation methods for melasma severity assessment.