Wavelet and Curvelet Features for Breast Tumor Detection Based on Digital Mammograms

• Main Authors: Ya-Liang Chang, 張雅量
• Other Authors: 余松年
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/14190397020325318374

碩士 === 國立中正大學 === 電機工程研究所 === 102 === Nowadays, mammogram is the most effective breast tumor detection method. In this research, we used the Mini-Mammographic Database provided by the Mammographic Image Analysis Society (MIAS). There are 322 digital images of left and right breasts from 161 patients. We used 117 of them that contained tumor image for experiments. This research employed totally 126 texture features, including 4 from Gray Level Histogram, 44 from Spatial Gray Level Dependence, 9 from Texture Spectrum, 48 from Texture Feature Coding Method, 5 from Neighboring Gray Level Dependence Textural Feature, and 16 from Gray Level Run-Length Textural Features. Apart from the commonly used texture features, we recruited wavelet features and the curvelet features which were developed in recent years. In the study, to select the most representative features, we used the Fisher’s linear discriminant analysis to rank the features and selected the most discriminative 10~15 features. The support vector machine was employed as the classifiers. In the classification apart, using the entire texture features, an accuracy is of 99.4% was attained in recognizing tumors in different types of tissues. By using the feature selection method, we can extensively decrease the amount of features and keep the effective features. An accuracy of 92.9% was retained by using only 10 effective features. Moreover, adding wavelet features enhanced the accuracy up to 98.2%. Further adding curvelet features elevated the accuracy to 99.1%. In the detection part, we first studied the influence of matrix sizes. Among the three matrix sizes, 32x32, 64x64 and 128x128, matrix of size 64x64 contributed to the best results. Most of the tumors could be detected, especially tumors embedded in the fatty tissues. The false negative was 1.31/image. Comparatively, tumors were relatively difficult to be detected in dense glandular tissue. The false negative for dense glandular and fatty glandular tissue were 1.67/image and 1.38/image, respectively. The proposed method was demonstrated to be effective in assisting doctors in diagnosizing breast tumors. By enhancing the diagnosis accuracy, the mortality rate of the patients can be reduced.