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摘 要:针对基于细胞图像分割的病变宫颈细胞筛查中由于细胞精细分割复杂而不能实现筛查自动化的问题,提出一种省略精细分割步骤的宫颈细胞分类算法。首先,定义一种新的用于描述像素值分布的特征——最大截面(MAXSection)特征,将该特征与反向传播(BP)神经网络和Selective Search算法结合,实现细胞核感兴趣区域(ROI)的准确提取(最高正确率100%);其次,基于最大截面特征定义了两个参数——估计长与估计宽,用于描述病变细胞核的形态变化;最后,根据宫颈细胞发生癌变时其核会绝对增大的特点,利用以上两参数实现病变细胞核(估计长与估计宽中至少一个参数大于65)与正常细胞核(估计长与估计宽均小于等于65)的分类。实验结果表明,该自动筛查算法的准确率为98.89%,敏感度为98.18%,特异度为99.20%。该算法可以完成从输入整幅巴氏涂片到输出最终筛查结果的全部过程,实现病变宫颈细胞筛查的自动化。
关键词:病变宫颈细胞筛查;精细分割;反向传播神经网络;Selective Search算法
中图分类号:TP391.7
文献标志码:A
文章编号:1001-9081(2019)04-1189-07
Abstract: Aiming at the problem that the complexity of cervical cell image fine segmentation makes it difficult to achieve automatic abnormal cell screening based on cell image segmentation, a cervical cell classification algorithm without fine segmentation step was proposed. Firstly, a new feature named MAXimum Section (MAXSection) was defined for describing the distribution of pixel values, and was combined with Back Propagation (BP) neural network and Selective Search algorithm to realize the accurate extraction of nucleus Region Of Interest (ROI) (the highest accuracy was 100%). Secondly, two parameters named estimated length and estimated width were defined based on MAXSection to describe morphological changes of abnormal nucleus. Finally, according to the characteristic of absolute enlargement of cervical nucleus when cervical cancer occurs, the classification of abnormal nucleus (at least one parameter of estimated length and width is greater than 65) and normal nucleus (estimated length and width are both less than 65) can be realized by using the above two parameters. Experimental results show that the proposed algorithm has screening accuracy of 98.89%, sensitivity of 98.18%, and specificity of 99.20%. The proposed algorithm can complete the total process from the input of whole Pap smear image to the output of final screening results, realizing the automation of abnormal cervical cell screening.
Key words: abnormal cervical cell screening; fine segmentation; Back Propagation (BP) neural network; Selective Search algorithm
0 引言
世界衛生组织国际癌症研究机构(International Agency for Research on Cancer)统计数据显示,全球范围内宫颈癌的发病率、患病率和死亡率在所有女性罹患的癌症中均位居前五[1]。2018年2月,国家癌症中心发布了最新的全国癌症统计数据:在我国,宫颈癌的发病率在所有女性罹患的恶性肿瘤中排名第六[2]。而宫颈癌的发生和发展有一个渐进的演变过程,早期的癌前病变的治愈率非常高,因此宫颈癌的预防与筛查显得尤为重要。基于巴氏涂片或液基细胞学涂片的细胞学筛查被认为是最常用且有效的方法,其操作简单、成本低廉,通过人工或者计算机辅助手段完成宫颈病变筛查。人工阅片方法受限于阅片人的临床经验及阅片人其他的主观意识等因素,与计算机辅助阅片相比,其工作效率较低。对计算机辅助细胞学检查的研究从20世纪开始,通过结合计算机技术与病理学知识,诊断由于细胞病变而引发的癌症。