Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009 |
| Pages | 209-214 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2009 Nov 4 |
| Event | 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009 - Singapore, Singapore Duration: 2009 Jul 14 → 2009 Jul 17 |
Publication series
| Name | IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM |
|---|
Other
| Other | 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 09-07-14 → 09-07-17 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Software
- Computer Science Applications
- Electrical and Electronic Engineering
Cite this
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Quantitative analysis of micro-calcifications for breast cancer via wavelet transform and neural network. / Tsai, Nan-Chyuan; Chen, Hong Wei; Hsu, Sheng Liang.
2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009. 2009. p. 209-214 5230014 (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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 - 2009/11/4
Y1 - 2009/11/4
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
UR - http://www.scopus.com/inward/citedby.url?scp=70350459409&partnerID=8YFLogxK
U2 - 10.1109/AIM.2009.5230014
DO - 10.1109/AIM.2009.5230014
M3 - Conference contribution
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
ER -