Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 86, Issue -, Pages 135-144Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2017.05.062
Keywords
Microcalcification diagnosis; Digital mammography; Dual-tree complex wavelet transform; Hidden Markov tree model; Extreme learning machine; Feature extraction
Categories
Funding
- National Natural Science Foundation of China [61172171, 61401386]
- Scientific Research Fund of Hunan Provincial Education Department [16C1545]
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Diagnosis of benign and malignant microcalcifications in digital mammography using Computer-aided Diagnosis (CAD) system is critical for the early diagnosis of breast cancer. Wavelet transform based diagnosis methods are effective to accomplish this task, but limited by representing the correlation within each wavelet scale, these methods neglect the correlation between wavelet scales. In this paper, we apply the hidden Markov tree model of dual-tree complex wavelet transform (DTCWT-HMT) for microcalcification diagnosis in digital mammography. DTCWT-HMT can effectively capture the correlation between different wavelet coefficients and model the statistical dependencies and non-Gaussian statistics of real signals, is used to characterize microcalcifications for the diagnosis of benign and malignant cases. The combined features which consist of the DTCWT-HMT features and the DTCWT features are optimized by genetic algorithm (GA). Extreme learning machine (ELM), an efficient learning theory is employed as the classifier to diagnose the benign and malignant microcalcifications. The validity of the proposed method is evaluated on the Nijmegen, MIAS and DDSM datasets using area under curve (AUC) of receiver operating characteristic (ROC). The AUC values of 0.9856, 0.9941 and 0.9168 of the proposed method are achieved on Nijmegen, MIAS and DDSM, respectively. We compare the proposed method with state-of-the-art diagnosis methods, and the experimental results show the effectiveness of the proposed method for the diagnosis of the benign and malignant microcalcifications in mammograms in terms of the accuracy and stability. (C) 2017 Elsevier Ltd. All rights reserved.
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