원문정보
초록
영어
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
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