Nucleus Classification and Recognition of Uterine Cervical Pap-Smears Using FCM Clustering Algorithm

Authors:
Kwang-Baek Kim;Sungshin Kim;Gwang-Ha Kim
Affiliations:
Dept. of Computer Engineering, Silla University, Busan, Korea;School of Electrical Engineering, Pusan National University, Busan, Korea;Dept. of Internal Medicine, Pusan National University College of Medicine, Busan, Korea
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Year:
2007

Citing 3
Cited 0

Fuzzy sets and fuzzy logic: theory and applications

Fuzzy sets and fuzzy logic: theory and applications
Texture Features for Browsing and Retrieval of Image Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

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 nucleus region 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.