Classifying mammographic mass shapes using the wavelet transform modulus-maxima method.
In this article, multiresolution analysis, specifically the discrete wavelet transform modulus-maxima (mod-max) method, is utilized for the extraction of mammographic mass shape features. These shape features are used in a classification system to classify masses as round, nodular, or stellate. The multiresolution shape features are compared with traditional uniresolution shape features for their class discriminating abilities. The study involved 60 digitized mammographic images. The masses were segmented manually by radiologists, prior to introduction to the classification system. The uniresolution and multiresolution shape features were calculated using the radial distance measure of the mass boundaries. The discriminating power of the shape features were analyzed via linear discriminant analysis (LDA). The classification system utilized a simple Euclidean metric to determine class membership. The system was tested using the apparent and leave-one-out test methods. The classification system when using the multiresolution and uniresolution shape features resulted in classification rates of 83% and 80% for the apparent and leave-one-out test methods, respectively. In comparison, when only the uniresolution shape features were used, the classification rates were 72 and 68% for the apparent and leave-one-out test methods, respectively.
Bruce
L M
LM
Department of Electrical and Computer Engineering, University of Nevada Las Vegas, 89154, USA.
Adhami
R R
RR
D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008327:Mammography / Q000145:classification* / Q000706:statistics & numerical data; D015233:Models, Statistical