TY - GEN
T1 - Quantitative analysis of micro-calcifications for breast cancer via wavelet transform and neural network
AU - Tsai, Nan Chyuan
AU - Chen, Hong Wei
AU - Hsu, Sheng Liang
PY - 2009
Y1 - 2009
N2 - A high-sensitivity computer-aided diagnosis algorithm which can detect and quantify micro-calcifications for early-stage breast cancer is proposed in this research. The algorithm can be divided into two phases: image reconstruction and recognition on micro-calcification regions. For Phase I, the suspicious micro-calcification regions are separated from the normal tissues by wavelet layers and Renyi's information theory. The Morphology-Dilation and Majority Voting Rule are employed to reconstruct the scattered regions of suspicious micro-calcification. For Phase II, total 49 descriptors which mainly includes shape inertia, compactness, eccentricity and grey-level co-occurrence matrix are introduced to define the characteristics of the suspicious micro-calcification clusters. In order to reduce the computation load, principal component analysis is used to transform these descriptors to a compact but efficient expression by linear combination method. The performance of proposed diagnosis algorithm is verified by intensive experiments upon realistic clinic patients. The efficacy of back-propagation neural network classifier exhibits its superiority in terms of high true positive rate(TP rate) and low false positive(FP rate) rate, in comparison to Bayes classifier.
AB - A high-sensitivity computer-aided diagnosis algorithm which can detect and quantify micro-calcifications for early-stage breast cancer is proposed in this research. The algorithm can be divided into two phases: image reconstruction and recognition on micro-calcification regions. For Phase I, the suspicious micro-calcification regions are separated from the normal tissues by wavelet layers and Renyi's information theory. The Morphology-Dilation and Majority Voting Rule are employed to reconstruct the scattered regions of suspicious micro-calcification. For Phase II, total 49 descriptors which mainly includes shape inertia, compactness, eccentricity and grey-level co-occurrence matrix are introduced to define the characteristics of the suspicious micro-calcification clusters. In order to reduce the computation load, principal component analysis is used to transform these descriptors to a compact but efficient expression by linear combination method. The performance of proposed diagnosis algorithm is verified by intensive experiments upon realistic clinic patients. The efficacy of back-propagation neural network classifier exhibits its superiority in terms of high true positive rate(TP rate) and low false positive(FP rate) rate, in comparison to Bayes classifier.
UR - http://www.scopus.com/inward/record.url?scp=70350459409&partnerID=8YFLogxK
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U2 - 10.1109/AIM.2009.5230014
DO - 10.1109/AIM.2009.5230014
M3 - Conference contribution
AN - SCOPUS:70350459409
SN - 9781424428533
T3 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
SP - 209
EP - 214
BT - 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009
T2 - 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009
Y2 - 14 July 2009 through 17 July 2009
ER -