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.

基于结构熵和孤立森林异常检测的新型无监督皮肤病变分割框架 SLED，通过构建皮肤镜图像的超像素图，最小化结构熵实现皮肤病变的分割，并通过多尺度的异常检测机制增强分割的准确性。实验结果在四个皮肤病变数据集上表明了所提出框架的优越性。此外，对一些案例进行了分析，展示了 SLED 的有效性。