FCF-CSM: A Fuzzy Clustering Framework Based on Chromaticity Statistical Model for Automatic Segmentation of Port Wine Stains

Extracting lesions with accurate boundaries from clinical images is crucial for the clinical diagnosis, progression monitoring, and efficacy evaluation of port wine stains (PWS). However, accurately delineating lesion boundaries remains challenging due to complex boundary structures and unreliable annotations. In this paper, we propose a fuzzy clustering framework, FCF-CSM, for automated segmentation of PWS lesions. It combines prior knowledge of PWS color distribution with superpixels’ boundary characterization capability. Firstly, a chromaticity statistical model (CSM) is established based on 1000 collected PWS images, providing prior probabilities of PWS lesions to guide the improvement of the fuzzy clustering framework. Secondly, a superpixel method incorporating CSM is applied to PWS images, generating superpixels with improved boundary characterization. Thirdly, these superpixels are finely clustered using color features related to erythema index, color statistics, and color volume, improving PWS lesion distinction from complex backgrounds. Finally, a CSM-based automatic decision method distinguishes lesions from the background, achieving fully automated PWS segmentation within a fuzzy clustering framework. In addition, a boundary local fitting (BLF) metric is proposed to evaluate the segmentation precision of the PWS boundaries. Comparative experiments are conducted to verify the superiority of FCF-CSM. It achieves comparable overall segmentation performance with Jaccard and Dice metrics of 84.14% and 91.11%, respectively, compared to state-of-the-art methods. In terms of boundary segmentation, FCF-CSM outperforms other methods with an 81.52% BLF metric. FCF-CSM has proven to be effective for PWS segmentation and is promising to improve boundary delineation.