A microcalcification detection method based on wavelet singularity was presented because of microcalcification singularity characteristic. Firstly, the source image is decomposed in multi-scales wavelet coefficients. Secondly, coefficients in low-pass band are removed and coefficients in high-pass band are enhanced contrast by nonlinear method. Lastly, fisher discriminant was adopted in segment microcalcifications. Experiment results showed that wavelet basis with shorter support and lower regularity is more sensitive to noise, while wavelet basis with longer support, higher regularity and higher order vanishing moment could segment indistinct microcalcifications, but sometime could not segment small microcalcifications. The results also showed the detect effect DAUB4 wavelet is best and its detection ratio is about 96%.
목차
Abstract 1. Foreword 2. Theory of Wavelet Singularity 2.1. Signal Singularity 2.2. Choice of Wavelet Basic Function 3. Enhanced Wavelet Coefficient 3.1. 2D Image Wavelet Transform 3.2. Enhanced Wavelet Coefficient 4. Microcalcification Segment Discriminant Based on Fisher 5. Experimental Result 6. Concluding Remarks Acknowledgements References
보안공학연구지원센터(IJSIP) [Science & Engineering Research Support Center, Republic of Korea(IJSIP)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Signal Processing, Image Processing and Pattern Recognition
간기
격월간
pISSN
2005-4254
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.7 No.1