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A Novel Breast mass segmentation method based on patch merging and GHFCM

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

저자정보

  • 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

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