Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Dermoscopy Images
Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Dermoscopy Images
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DOI：
10.1109/jstsp.2008.2010631
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发表时间：
2009-02-01
影响因子：
7.5
通讯作者：
Celebi, M. Emre
中科院分区：
文献类型：
--
作者：
Zhou, Huiyu;Schaefer, Gerald;Celebi, M. Emre
图像分割是分析皮肤镜图像的一项重要任务，因为皮肤病变边缘的提取为准确诊断提供了重要线索。一类分割算法是基于具有相似特征的像素聚类的思想。模糊c-means已经被证明可以很好地用于基于聚类的分割，但是由于它的迭代性质，这种方法有过多的计算需求。在本文中，我们引入了一种新的基于均值移位的模糊c均值算法，该算法比以前的技术需要更少的计算时间，同时提供了良好的分割结果。所提出的分割方法在标准模糊c均值目标函数中加入了一个平均字段项。由于mean shift可以快速可靠地找到聚类中心，因此整个策略能够有效地检测图像内的区域。在不同皮肤镜图像的大数据集上的实验结果表明，该方法能够准确有效地检测皮肤病变的边界。
Image segmentation is an important task in analysing dermoscopy images as the extraction of the borders of skin lesions provides important cues for accurate diagnosis. One family of segmentation algorithms is based on the idea of clustering pixels with similar characteristics. Fuzzy c-means has been shown to work well for clustering based segmentation, however due to its iterative nature this approach has excessive computational requirements. In this paper, we introduce a new mean shift based fuzzy c-means algorithm that requires less computational time than previous techniques while providing good segmentation results. The proposed segmentation method incorporates a mean field term within the standard fuzzy c-means objective function. Since mean shift can quickly and reliably find cluster centers, the entire strategy is capable of effectively detecting regions within an image. Experimental results on a large dataset of diverse dermoscopy images demonstrate that the presented method accurately and efficiently detects the borders of skin lesions.