퍼지 클러스터 기반 디지털 유방 X선 영상 진단 시스템
Fuzzy Cluster Based Diagnosis System for Digital Mammogram
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
최근 ACS에 따르면 여성에게 유방암은 가장 많이 발병하는 암으로서 그 사망자 수도 두 번째로 많은 암이다. 유방 X선 영상의 종괴나 석회 환부는 진단을 위한 가장 중요한 단서로서 알려져 있으므로 유방암의 조기진단을 위하여 디지털 유방 X선 영상을 컴퓨터에서 처리하는 연구가 진행되고 있다. 본 논문에서는 퍼지 클러스터 지식베이스에 기반을 둔 진단시스템을 제안한다. 제안된 시스템은 듀얼 OFUN-NET에 두 가지 종류의 특징데이터를 처리하여 진단결과와 그 가능성을 알려준다. 실세계 의료기관으로부터 수집되고 공개적으로 제공되는 유방 X선 데이터베이스 DDSM으로부터 획득한 종괴와 석회 환부의 데이터를 사용하여 실험한다. 실험결과는 제안된 시스템이 기존의 방법보다 높은 분류 정확도와 유방 X선 영상 진단시스템으로서 전문가의 의사 결정을 도울 수 있는 타당한 결과를 보여준다.
Abstract
According to the American Cancer Society, breast cancer is the second largest cause of cancer deaths and most frequently diagnosed cancer in women. The currently most popular method for early detection of breast cancer is the digital mammography. A mass or calcification lesion has been known as the most important clue for the diagnosis. In this paper, we propose a diagnosis approach based on fuzzy cluster knowledge base. We combine different two sources of feature data in duel OFUN-NET and produce the diagnosis result with possibility degree. We also present the experimental results on the dataset of mass and calcification lesions extracted from the public real world mammogram database DDSM. These results show higher classification accuracy than conventional methods and the feasibility as a decision supporting tool for diagnosis of digital mammogram.
참고문헌 (18)
- A. C. Society, "Cancer Facts and Figures" (2008), American Cancer Society
- L. Wei, "A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications", IEEE Trans. on Medical Imaging, 24, pp.371~380 (2005)
- I. El-Naqa, "A support vector machine approach for detection of microcalcifications", IEEE Trans. on Medical Imaging, 21(12), pp.1552~1563 (2002)
- Heath, M, "The Digital Database for Screening Mammography" (2001), Medical Physics Publishing
- "A.C. of Radiology, Breast Imaging Reporting and Data System(BI-RADS)", American College of Radiology, ~ (1998)
- Ping Zhang, "Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection", pp.909~919 (2005), Pattern Recognition Letters26
- S. Kim, "Mass lesions classification in digital mammography using optimal subset of bi-rads and gray level features", in IEEE proceedings of ITAB2007, pp.99~102 (2007)
- Baker, J.A, "Breast Cancer: Prediction with Artificial Neural Network based on BI-RADS Standardized Lexicon", Radiology, 196(3), pp.817~822 (1995)
- Bovis, K, "dentification of Masses in Digital Mammograms with MLP andRBF Nets", proc. of the IEEE International Joint Conference on Neural Networks (IEEE-INNS-ENNS’2000) (2000)
- Edwards, D.C, "Estimating Three Class Ideal Observer Decision Variables for Computerized Detection and Classification of Mammographic Mass Lesions", Medical Physics, 31(1), pp.81~90 (2004)
- Wu, Y, "Neural Network Fusion Strategies for Identifying Breast Masses", proc. of the IEEE International Joint Conference on Neural Networks (IEEE-IJCNN’2004) (2004)
- R. Panchal, "Characterization of breast abnormality patterns in digital mammograms using autoassociator neural network", in International Conference on Image Processing 2006, Part III, LNCS, 통권 : 4234, pp.127~136 (2006), Springer-Verlag
- Brijesh Verma, "A Computer-Aided Diagnosis System for Digital Mammograms Based on Fuzzy-Neural and Feature Extraction Techniques", IEEE Trans. on Information Technology in Biomedicine, 5(1) (2001)
- J.Y. Lo, "Computer-aided classification of breast microcalcification clusters: Merging of features from image processing and radiologists", ~ (2003)
- Yi-Ta Wu, "A Data Mining Based Genetic Algorithm", Proc. og the 4th IEEE workshop on SEUS-WCCIA (2006)
- Jian Yu, "Optimality Test for Generalized FCM and Its Application to Parameter Selection", IEEE Transactions on Fuzzy Systems, 13(1) (2005)
- 이현숙, "컴퓨터 바이러스 분류를 위한 퍼지 클러스터 기반 진단시스템", 정보처리학회논문지B, 14(1), pp.59~64 (2007), 한국정보처리학회
- 이현숙, "퍼지 클러스터 분석 기반 특징 선택 방법", 정보처리학회논문지B, 14(2), pp.135~140 (2007), 한국정보처리학회