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

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