Cervical cancer is identified as the fourth most recurrent cancer among women across the globe. The cancer is treatable, if identified at the early stage. Pap smear test is the most common and the best tool for initial screening of cancer. Pap smear cell level image analysis is an open issue. The limitation of the analysis is due to the complexity of the cell structure. The smear cell image is composed of cytoplasm and nucleus. The shape and structure of the nucleus determines the cancer prevalence. Segmentation of nucleus is an important step in cancer detection. There are various methods developed for nucleus segmentation. The article proposes multithresholding algorithm to segment cytoplasm and nucleus region from the background. Morphological operations are used for correcting the segmented output. Support vector machine classifier is used for classifying the smear cell as normal or abnormal based on the extracted features of the segmented output. The obtained accuracy of the classifier, sensitivity and specificity for single smear cell are 99.66%, 99.85% and 99.17%.

中文翻译：

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从巴氏涂片细胞图像中自动分割细胞核：迈向宫颈癌筛查的一步

宫颈癌被确定为全球女性中第四大复发癌症。如果在早期发现，癌症是可以治疗的。子宫颈抹片检查是最常见和最好的癌症初步筛查工具。巴氏涂片细胞水平图像分析是一个悬而未决的问题。分析的局限性是由于单元结构的复杂性。涂片细胞图像由细胞质和细胞核组成。细胞核的形状和结构决定了癌症的患病率。细胞核的分割是癌症检测的重要步骤。已经开发了各种用于细胞核分割的方法。文章提出了多阈值算法从背景中分割细胞质和细胞核区域。形态学操作用于校正分段输出。支持向量机分类器用于根据分割输出的提取特征将涂片细胞分类为正常或异常。获得的分类器对单个涂片细胞的准确度、灵敏度和特异性分别为99.66%、99.85%和99.17%。