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首页> 外文会议>2015 Global Conference on Communication Technologies >Nuclear segmentation for skin cancer diagnosis from histopathological images
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Nuclear segmentation for skin cancer diagnosis from histopathological images

机译：从组织病理学图像诊断皮肤癌的核分割

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Skin cancer is the most frequent and malignant type of cancer. Melanoma is the most aggressive type among skin cancers and if they are detected at an early stage, they can be completely cured. In melanoma diagnosis, the detection of the melanocytes in the epidermis area is an important step. For the detection of melanocytes, use of histopathological images can be used. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. The digitized images are analysed with advanced image segmentation algorithms and features such as intensity and size of the cell nuclei is used to filter out the candidate nuclei regions. This paper deals with Enhancement, Segmentation and Classification in histopathological images of the skin. The proposed method uses CLAHE algorithm for the image enhancement followed by bilateral filtering. The initial segmentation is achieved through Fuzzy C-Means algorithm and a local region recursive algorithm is performed for the final segmentation results. Elliptical derscriptor is used to obtain region ellipticity and local pattern characteristics to distinguish the melanocytes from the candidate nuclei regions.
机译：皮肤癌是最常见和恶性的癌症。黑色素瘤是皮肤癌中最具侵略性的类型，如果在早期发现它们，就可以完全治愈。在黑色素瘤诊断中，检测表皮区域的黑色素细胞是重要的一步。为了检测黑素细胞，可以使用组织病理学图像。随着最近的全玻片数字扫描仪的出现，组织组织病理学玻片现在可以数字化并以数字图像形式存储。使用先进的图像分割算法对数字化图像进行分析，并使用诸如细胞核强度和大小之类的特征来滤除候选细胞核区域。本文涉及皮肤组织病理学图像的增强，分割和分类。所提出的方法使用CLAHE算法进行图像增强，然后进行双边滤波。通过Fuzzy C-Means算法实现初始分割，并对最终分割结果执行局部递归算法。椭圆描述符用于获得区域椭圆度和局部模式特征，以区分黑素细胞与候选细胞核区域。

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