Automatic screening of cervical cells using block image processing. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Zhao, Meng; Wu, Aiguo; Song, Jingjing; Sun, Xuguo; Dong, Na
• Is Part Of: Biomedical engineering online. Volume 15:Issue 1(2016)
• Page Start: 1
• Page End: 20
• Publication Date: 2016-12
• Subjects: Cervical cells screening -- SVM -- Features selection -- Block image processing -- Computational complexity; Biomedical engineering -- Periodicals; 610.2805
• DOI: 10.1186/s12938-016-0131-z
• Languages: English
• ISSNs: 1475-925X

Abstract

Abstract Background Cervical cancer is the second leading cause of female-specific cancer-related deaths after breast cancer, especially in developing countries. However, the incidence of the disease may be significantly decreased if the patient is diagnosed in the pre-cancerous lesion stage or earlier. In recent years, computer-based algorithms are widely used in cervical cancer screening. Most of the proposed algorithms follow the procedure of segmentation, feature extraction, and then classification. Nevertheless, few of the existing segmentation methods are as flexible and robust as the human visual system, and the complexity of the algorithms makes it difficult for clinical application. Methods In this study, a computer-assisted analytical approach is proposed to identify the existence of suspicious cells in a whole slide cervical cell image (WSCCI). The main difference between our method and the conventional algorithm is that the image is divided into blocks with certain size instead of segmented cells, which can greatly reduce the computational complexity. Via data analysis, some texture and color histogram features show significant differences between blocks with and without suspicious cells. Therefore these features can be used as the input of the support vector machine classifier. 1100 non-background blocks (110 suspicious blocks) are trained to build a model, while 1040 blocks (491 non-background blocks) from 12 other WSCCIs are tested to verify the feasibility of the algorithm. Results The experimental results show that the accuracy of our method is about 98.98 %. More importantly, the sensitivity, which is more fatal in cancer screening, is 95.0 % according to the images tested in the study, while the specificity is 99.33 %. Conclusion The analysis of the algorithm is based on block images, which is different from conventional methods. Although some analysis work should be done in advance, the later processing speed will be greatly enhanced with the establishment of the model. Furthermore, since the algorithm is based on the actual WSCCI, the method will be of directive significance for clinical screening.