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Nucleus Recognition of Uterine Cervical Pap-Smears using FCM Clustering Algorithm  

Kim, Kwang-Baek (Division of Computer and Information Engineering, Silla University)
Publication Information
Journal of information and communication convergence engineering / v.6, no.1, 2008 , pp. 94-99 More about this Journal
Abstract
Segmentation for the region of nucleus in the image of uterine cervical cytodiagnosis is known as the most difficult and important part in the automatic cervical cancer recognition system. In this paper, the region of nucleus is extracted from an image of uterine cervical cytodiagnosis using the HSI model. The characteristics of the nucleus are extracted from the analysis of morphemetric features, densitometric features, colormetric features, and textural features based on the detected region of nucleus area. The classification criterion of a nucleus is defined according to the standard categories of the Bethesda system. The fuzzy C-means clustering algorithm is employed to the extracted nucleus and the results show that the proposed method is efficient in nucleus recognition and uterine cervical Pap-Smears extraction.
Keywords
Uterine Cervical Cytodiagnosis; Automatic Cervical Cancer Recognition System; HSI Model; Bethesda System; Fuzzy C-means Clustering Algorithm;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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