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首页> 外文会议>Recent advances in applied amp; biomedical informatics and computational engineering in systems applications >Classification of Cancer Cells Based on Morphological Features From Segmented MultiSpectral Bio-Images
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Classification of Cancer Cells Based on Morphological Features From Segmented MultiSpectral Bio-Images

机译：基于分段多光谱生物图像的形态特征对癌细胞进行分类

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In this paper a new approach aiming to detect and classify colon cancer cells is presented. Our detection approach was derived from the "Snake" method but using a progressive division of the dimensions of the image to achieve faster segmentation. Classification of different cell types was based on nine morphological parameters and on probabilistic neural network. Three types of cells were used to assess the efficiency of our segmentation and classifications models, including Benign Hyperplasia (BH), Intraepithelial Neoplasia (IN) that is a precursor state for cancer, and Carcinoma (Ca) that corresponds to abnormal tissue proliferation (cancer). Results showed that segmentation of microscopic images using this technique was of higher efficiency than the conventional snake method. The time consumed during segmentation was decreased to more than 50%. The efficiency of this method resides in its ability to segment Ca type cells that was difficult through other segmentation procedures. In classification only three morphologic parameters (area, Xor convex and solidity) were found to be effective to discriminate between the three types of cells. The results obtained using several images show the efficacy of the method.
机译：在本文中，提出了一种旨在检测和分类结肠癌细胞的新方法。我们的检测方法源自“ Snake”方法，但是使用图像尺寸的逐步划分来实现更快的分割。根据九种形态学参数和概率神经网络对不同细胞类型进行分类。三种类型的细胞用于评估我们的分割和分类模型的效率，包括良性增生（BH），上皮内瘤变（IN）（是癌症的前兆状态）和对应于异常组织增殖（癌）的癌（Ca） ）。结果表明，与传统的蛇形方法相比，使用这种技术对显微图像进行分割具有更高的效率。分割期间消耗的时间减少到50％以上。这种方法的效率在于其对Ca型细胞进行分割的能力，而这是其他分割程序难以做到的。在分类中，仅发现三个形态学参数（面积，异或凸性和坚固性）可有效区分这三种类型的细胞。使用几幅图像获得的结果表明了该方法的有效性。

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