Automated Segmentation of the Melanocytes in Skin Histopathological Images
Automated Segmentation of the Melanocytes in Skin Histopathological Images
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DOI：
10.1109/titb.2012.2199595
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发表时间：
2013-03-01
影响因子：
7.7
通讯作者：
Mandal, Mrinal
中科院分区：
文献类型：
--
作者：
Lu, Cheng;Mahmood, Muhammad;Mandal, Mrinal
在通过分析组织病理学图像诊断皮肤黑色素瘤的过程中，检测表皮区域的黑素细胞是重要的一步。然而，在表皮区域检测黑素细胞是困难的，因为也存在其他与黑素细胞非常相似的角质形成细胞。本文提出了一种新的计算机辅助分割皮肤组织病理图像中的黑素细胞的方法。为了减小局部灰度变化，采用均值漂移算法对图像进行初始分割。在此基础上，提出了一种基于区域先验知识的局部区域递归分割算法来筛选出候选核区域。为了将黑素细胞与其他角质形成细胞区分开来，提出了一种新的局部双椭圆描述符(LDED)来度量候选区域的局部特征。LDED使用两个参数：区域椭圆度和局部图案特征来区分黑素细胞和候选核区。对28幅不同放大倍数的皮肤组织病理图像的实验结果表明，该方法具有较好的分割效果。
In the diagnosis of skin melanoma by analyzing histopathological images, the detection of the melanocytes in the epidermis area is an important step. However, the detection of melanocytes in the epidermis area is difficult because other keratinocytes that are very similar to the melanocytes are also present. This paper proposes a novel computer-aided technique for segmentation of the melanocytes in the skin histopathological images. In order to reduce the local intensity variant, a mean-shift algorithm is applied for the initial segmentation of the image. A local region recursive segmentation algorithm is then proposed to filter out the candidate nuclei regions based on the domain prior knowledge. To distinguish the melanocytes from other keratinocytes in the epidermis area, a novel descriptor, named local double ellipse descriptor (LDED), is proposed to measure the local features of the candidate regions. The LDED uses two parameters: region ellipticity and local pattern characteristics to distinguish the melanocytes from the candidate nuclei regions. Experimental results on 28 different histopathological images of skin tissue with different zooming factors show that the proposed technique provides a superior performance.