Xiong Wei, Shi Zheng, Ong S H
Institute for Infocomm Research, A*STAR, Singapore 138632. wxiong@i2r,a-star.edu.sg
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:4002-5. doi: 10.1109/EMBC.2012.6346844.
Skin lesion segmentation in dermatoscopic images is difficult because there are large inter variations in shape, size, color, and texture between lesions and skin types. Hence, computational features learned from a training set of lesion images may not be applicable to other lesion images. In this paper, we propose an incremental method for lesion segmentation. It leverages the Expectation-Maximization algorithm to find an initial segmentation. A new adaptive method is proposed to define two types of segmented regions: the high-confident and the low-confident. We train a support vector machine, using computational features from the high-confident regions, to further refine segmentation and, hence, achieve improved results for the low-confident regions. Validation experiments of our proposed method are performed on 319 dermatoscopy images and we have achieved good results with precision and recall to be 0.864 and 0.875 respectively.
皮肤镜图像中的皮肤病变分割具有挑战性，因为病变与不同皮肤类型之间在形状、大小、颜色和纹理上存在很大差异。因此，从病变图像训练集中学习到的计算特征可能不适用于其他病变图像。在本文中，我们提出了一种用于病变分割的增量方法。该方法利用期望最大化算法找到初始分割。我们提出了一种新的自适应方法来定义两种分割区域：高置信度区域和低置信度区域。我们使用高置信度区域的计算特征训练支持向量机，以进一步细化分割，从而提高低置信度区域的分割效果。我们在319张皮肤镜图像上对所提方法进行了验证实验，精确率和召回率分别达到了0.864和0.875，取得了良好的效果。
