Unsupervised Skin Lesion Segmentation via Structural Entropy
  Minimization on Multi-Scale Superpixel Graphs
Guangjie Zeng Hao Peng Angsheng Li Zhiwei Liu Chunyang Liu Philip S. Yu Lifang He
Skin lesion segmentation is a fundamental task in dermoscopic image analysis.
The complex features of pixels in the lesion region impede the lesion
segmentation accuracy, and existing deep learning-based methods often lack
interpretability to this problem. In this work, we propose a novel unsupervised
Skin Lesion sEgmentation framework based on structural entropy and isolation
forest outlier Detection, namely SLED. Specifically, skin lesions are segmented
by minimizing the structural entropy of a superpixel graph constructed from the
dermoscopic image. Then, we characterize the consistency of healthy skin
features and devise a novel multi-scale segmentation mechanism by outlier
detection, which enhances the segmentation accuracy by leveraging the
superpixel features from multiple scales. We conduct experiments on four skin
lesion benchmarks and compare SLED with nine representative unsupervised
segmentation methods. Experimental results demonstrate the superiority of the
proposed framework. Additionally, some case studies are analyzed to demonstrate
the effectiveness of SLED.