Earticle

초록

영어

A hybrid microcalcification detection method based on Fisher discriminant and SVM was presented because signal-to-noise ratio of mammogram image was very low, and microcalcifications were very small and their shape was irregular. Firstly, low frequency information of tissue was removed by wavelet transform in order to reduce the tissue effect to microcalcification segment. Secondly, Fisher discriminant was adopted to find optimum threshold, meanwhile microcalcification was segmented. Lastly, SVM classifier was adopted to recognize true microcalcifications. Experiment results showed that Fisher discriminant could validly segment microcalcifications and the number of false positive targets was less than OSTU’s. Detection ratio of our algorithm was about 97%.

목차

Abstract
 1. Introduction
 2. Microcalcification Segment Based on Fisher Discriminant
  2.1. Fisher Discriminant
  2.2. Microcalcification Segmentation
 3. Microcalcification Detection Based on SVM
  3.1. Theory of SVM
  3.2. Feature Extraction
 4. Experimental Result
 5. Concluding Remarks
 Acknowledgements
 References

키워드

저자

• Guo Jinghuan [ Institute of Information Sciences and Technology, Dalian Maritime University ]
• Chen Shenglai [ The 28th Research Institute of China Electronics Technology Group Corporation ]
• Ge Ku [ Institute of Information Sciences and Technology, Dalian Maritime University ]
• Sun Zhaoqian [ Institute of Information Sciences and Technology, Dalian Maritime University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605