Unsupervised segmentation of cervical cell images using Gaussian Mixture Model

被引：27

作者：

Ragothaman, Srikanth ^{[1
]}

Narasimhan, Sridharakumar ^{[1
]}

Basavaraj, Madivala G. ^{[1
]}

Dewar, Rajan ^{[2
]}

机构：

[1] Indian Inst Technol Madras, Madras, Tamil Nadu, India

[2] Univ Michigan, Dept Pathol, Ann Arbor, MI 48109 USA

来源：

PROCEEDINGS OF 29TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, (CVPRW 2016) | 2016年

关键词：

CYTOPLAST CONTOUR DETECTOR; NUCLEUS SEGMENTATION; CLASSIFICATION;

D O I：

10.1109/CVPRW.2016.173

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Cervical cancer is one of the leading causes of cancer death in women. Screening at early stages using the popular Pap smear test has been demonstrated to reduce fatalities significantly. Cost effective, automated screening methods can significantly improve the adoption of these tests worldwide. Automated screening involves image analysis of cervical cells. Gaussian Mixture Models (GMM) are widely used in image processing for segmentation which is a crucial step in image analysis. In our proposed method, GMM is implemented to segment cell regions to identify cellular features such as nucleus, cytoplasm while addressing shortcomings of existing methods. This method is combined with shape based identification of nucleus to increase the accuracy of nucleus segmentation. This enables the algorithm to accurately trace the cells and nucleus contours from the pap smear images that contain cell clusters. The method also accounts for inconsistent staining, if any. The results that are presented shows that our proposed method performs well even in challenging conditions.

引用

页码：1374 / 1379

页数：6

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