Machine Vision Laboratory, University of the West of England, Bristol, UK.
Skin Res Technol. 2012 Feb;18(1):77-87. doi: 10.1111/j.1600-0846.2011.00534.x. Epub 2011 May 6.
Early identification of malignant melanoma with the surgical removal of thin lesions is the most effective treatment for skin cancers. A computer-aided diagnostic system assists to improve the diagnostic accuracy, where segmenting lesion from normal skin is usually considered as the first step. One of the challenges in the automated segmentation of skin lesions arises from the fact that darker areas within the lesion should be considered separate from the more general suspicious lesion as a whole, because these pigmented areas can provide significant additional diagnostic information.
This paper presents, for the first time, an unsupervised segmentation scheme to allow the isolation of normal skin, pigmented skin lesions, and interesting darker areas inside the lesion simultaneously. An adaptive mean-shift is first applied with a 5D spatial colour-texture feature space to generate a group of homogenous regions. Then the sub-segmentation maps are calculated by integrating maximal similarity-based region merging and the kernel k-means algorithm, where the number of segments is defined by a cluster validity measurement.
The proposed method has been validated extensively on both normal digital photographs and dermoscopy images, which demonstrates competitive performance in achieving automatic segmentation. The isolated dark areas have proved helpful in the discrimination of malignant melanomas from atypical benign nevi. Compared with the results obtained from the asymmetry measure of the entire lesion, the asymmetry distribution of the isolated dark areas helped increase the accuracy of the identification of malignant melanoma from 65.38% to 73.07%, and this classification accuracy reached 80.77% on integrating both asymmetry descriptors.
The proposed segmentation scheme gives the lesion boundary closed to the manual segmentation obtained by experienced dermatologists. The initial classification results indicate that the study of the distributions of darker areas inside the lesions is very promising in characterizing melanomas.
通过手术切除薄病变来早期识别恶性黑色素瘤是治疗皮肤癌最有效的方法。计算机辅助诊断系统有助于提高诊断准确性，而将病变与正常皮肤区分开通常被认为是第一步。自动分割皮肤病变的一个挑战是病变内的较暗区域应与整体更可疑的病变分开考虑，因为这些色素区域可以提供重要的额外诊断信息。
本文首次提出了一种无监督分割方案，以允许同时分离正常皮肤、色素性皮肤病变和病变内的有趣较暗区域。首先应用自适应均值漂移算法，使用 5D 空间颜色-纹理特征空间生成一组均匀区域。然后通过集成最大相似性区域合并和核 k-均值算法计算子分割图，其中分段的数量由聚类有效性度量定义。
该方法已在正常数字照片和皮肤镜图像上进行了广泛验证，在实现自动分割方面表现出了竞争力。分离出的暗区有助于区分恶性黑色素瘤和非典型良性痣。与整个病变的不对称性测量结果相比，孤立暗区的不对称分布有助于将恶性黑色素瘤的识别准确率从 65.38%提高到 73.07%，并且通过整合两个不对称描述符，分类准确率达到 80.77%。
所提出的分割方案给出了接近经验丰富的皮肤科医生手动分割的病变边界。初步分类结果表明，研究病变内较暗区域的分布在特征化黑色素瘤方面非常有前景。
