Earticle

초록

영어

Breast cancer is regarded as one of the most frequent mortality causes among women. It is very important to create a system to diagnose suspicious masses in mammograms for early breast cancer detection. In this paper, we propose an automatic breast mass segmentation method based on patch merging method and generalized hierarchical Fuzzy C Means (GHFCM). The patch merging method is used to obtain the adaptive region of interest (ROI), while the GHFCM method which is able to overcome the drawbacks of effect of image noise and Euclidean distance FCM which is sensitive to outliers is used to obtain the precisely mass segmentation results. The new method is evaluated over MiniMIAS dataset. The segmentation performance from experimentations demonstrates that our method outperforms the other compared methods.

목차

Abstract
 1. Introduction
 2. Our Method
  2.1 ROI Generation using ISODATA Clustering and Patch Merging
  2.2 Breast Mass Segmentation in ROI using GHFCM
 3. Implementation and Experiment Results
 4. Conclusions
 ACKNOWLEDGEMENTS
 References

키워드

저자

• Shenghua Gu [ Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing 210044 ]
• Yunjie Chen [ College of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044 ]
• Jin Wang [ College of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044 ]
• Jeong-Uk Kim [ Department of Energy Grid, Sangmyung University, Seoul 110-743, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605