Computer-aided diagnosis for early-stage breast cancer by using Wavelet Transform.
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 include 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 (PCA) is used to transform these descriptors to a compact but efficient vector 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 rate (FP rate), in comparison to Bayes classifier.
Tsai
Nan-Chyuan
NC
Department of Mechanical Engineering, National Cheng Kung University, Tainan City, Taiwan. nortren@mail.ncku.edu.tw
Chen
Hong-Wei
HW
Hsu
Sheng-Liang
SL
D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted; D055088:Early Detection of Cancer; D005260:Female; D006801:Humans; D009415:Nerve Net; D025341:Principal Component Analysis; D011859:Radiography; D058067:Wavelet Analysis