首页> 外文会议>Adaptive and Natural Computing Algorithms pt.2; Lecture Notes in Computer Science; 4432 >Nucleus Classification and Recognition of Uterine Cervical Pap-Smears Using FCM Clustering Algorithm
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Nucleus Classification and Recognition of Uterine Cervical Pap-Smears Using FCM Clustering Algorithm

机译:基于FCM聚类算法的子宫宫颈子宫颈涂片的核分类与识别

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Segmentation for the region of nucleus in the image of uterine cervical cytodiagnosis is known as the most difficult and important part in the automatic cervical cancer recognition system. In this paper, the nucleus region is extracted from an image of uterine cervical cytodiagnosis using the HIS model. The characteristics of the nucleus are extracted from the analysis of morphemet-ric features, densitornetric features, colormetric features, and textural features based on the detected region of nucleus area. The classification criterion of a nucleus is defined according to the standard categories of the Bethesda system. The fuzzy c-means clustering algorithm is employed to the extracted nucleus and the results show that the proposed method is efficient in nucleus recognition and uterine cervical Pap-Smears extraction.
机译:在宫颈宫颈细胞诊断图像中对核区域的分割被称为自动宫颈癌识别系统中最困难也是最重要的部分。在本文中,使用HIS模型从子宫宫颈细胞诊断图像中提取细胞核区域。根据检测到的核区域区域,通过对语素特征,光敏特征,色度特征和纹理特征的分析来提取原子核的特征。核的分类标准是根据Bethesda系统的标准类别定义的。将模糊c均值聚类算法应用于提取的核,结果表明该方法在核识别和子宫子宫颈抹片涂片提取中是有效的。

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