Epidermis segmentation in skin histopathological images based on thickness measurement and k-means algorithm
Epidermis segmentation in skin histopathological images based on thickness measurement and k-means algorithm
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DOI：
10.1186/s13640-015-0076-3
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发表时间：
2015-06-23
影响因子：
2.4
通讯作者：
Mandal, Mrinal
中科院分区：
文献类型：
--
作者：
Xu, Hongming;Mandal, Mrinal
皮肤组织病理图像中表皮区域的自动分割是各种皮肤癌计算机辅助诊断的重要步骤。本文提出了一种鲁棒的技术，表皮分割在整个幻灯片的皮肤组织病理学图像。该技术首先使用全局阈值和形状分析进行粗糙的表皮分割。然后通过垂直于初始分割的表皮掩模的主轴的一系列线段来测量表皮厚度。如果分割的表皮掩模具有大于预定义阈值的厚度，则假设分割是不准确的。然后，使用k-means算法对这些粗分割结果进行第二次精细分割，以提高性能。对64幅不同皮肤组织病理学图像的实验结果表明，与现有技术相比，该方法具有上级性能。
Automatic segmentation of the epidermis area in skin histopathological images is an essential step for computer-aided diagnosis of various skin cancers. This paper presents a robust technique for epidermis segmentation in the whole slide skin histopathological images. The proposed technique first performs a coarse epidermis segmentation using global thresholding and shape analysis. The epidermis thickness is then measured by a series of line segments perpendicular to the main axis of the initially segmented epidermis mask. If the segmented epidermis mask has a thickness greater than a predefined threshold, the segmentation is assumed to be inaccurate. A second pass of fine segmentation using k-means algorithm is then carried out over these coarsely segmented result to enhance the performance. Experimental results on 64 different skin histopathological images show that the proposed technique provides a superior performance compared to the existing techniques.