多特征聚类与粘连分离模型的细胞抹片图像分割与分类

胰腺癌的诊断非常重要,而细胞抹片显微图像的病理分析是其诊断的主要手段。图像的准确自动分割和分类是病理分析的重要环节,因此本文提出了一种新的胰腺细胞抹片显微图像自动分割与分类算法。在分割方面,首先采用多特征Mean-shift聚类算法（MFMS）定位细胞核区域;接着采用弹性数学形态学结合角点检测的去粘连模型（CSM）对粘连重叠细胞核进行去粘连处理,实现了分割的准确性和鲁棒性。在分类方面,首先针对分割的细胞核提取了4个形状特征和138个不同颜色空间的纹理特征;然后结合支持向量机（SVM）和链式遗传算法（CAGA）实现封装式特征选择;最后将优选特征送入SVM进行分类,完成了胰腺细胞抹片显微图像的分类...

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Published inSheng wu yi xue gong cheng xue za zhi Vol. 34; no. 4; pp. 614 - 621
Main Author 王品 刘倩倩 王力锐 李勇明 刘书君 颜芳
Format Journal Article
LanguageChinese
English
Published 中国四川 四川大学华西医院 01.08.2017
Subjects
Mean-shift聚类算法
新技术与新方法
细胞抹片显微图像分割
胰腺细胞
链式遗传算法
Online AccessGet full text
ISSN1001-5515
DOI10.7507/1001-5515.201605004

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Summary:胰腺癌的诊断非常重要,而细胞抹片显微图像的病理分析是其诊断的主要手段。图像的准确自动分割和分类是病理分析的重要环节,因此本文提出了一种新的胰腺细胞抹片显微图像自动分割与分类算法。在分割方面,首先采用多特征Mean-shift聚类算法（MFMS）定位细胞核区域;接着采用弹性数学形态学结合角点检测的去粘连模型（CSM）对粘连重叠细胞核进行去粘连处理,实现了分割的准确性和鲁棒性。在分类方面,首先针对分割的细胞核提取了4个形状特征和138个不同颜色空间的纹理特征;然后结合支持向量机（SVM）和链式遗传算法（CAGA）实现封装式特征选择;最后将优选特征送入SVM进行分类,完成了胰腺细胞抹片显微图像的分类识别。本文采用了15幅图像一共461个细胞核进行测试。实验结果显示,本文算法可以实现不同类型的胰腺细胞抹片显微图像的自动分割与准确分类。就分割来说,本文算法可获得较高的正确率（93.46%±7.24%）;就正常和癌变细胞的分类来说,本文算法可获得较高的分类正确率（96.55%±0.99%）、灵敏度（96.10%±3.08%）和特异度（96.80%±1.48%）。
Bibliography:The diagnosis of pancreatic cancer is very important. The main method of diagnosis is based on pathological analysis of microscopic image of Pap smear slide. The accurate segmentation and classification of images are two important phases of the analysis. In this paper, we proposed a new automatic segmentation and classification method for microscopic images of pancreas. For the segmentation phase, firstly multi-features Mean-shift clustering algorithm （MFMS） was applied to localize regions of nuclei. Then, chain splitting model （CSM） containing flexible mathematical morphology and curvature scale space corner detection method was applied to split overlapped cells for better accuracy and robustness. For classification phase, 4 shape-based features and 138 textural features based on color spaces of cell nuclei were extracted. In order to achieve optimal feature set and classify different cells, chain-like agent genetic algorithm （CAGA） combined with support vector machine （SVM） was proposed. The proposed method
ISSN:1001-5515
DOI:10.7507/1001-5515.201605004