<title>Wavelet-based feature extraction for mammographic lesion recognition</title>

Abstract

In this paper, multiresolution analysis, specifically the discrete wavelet transform modulus-maximus method, is utilized for the extraction of mammographic lesion shape features. These shape features are used in a classification system to classify lesions as cysts, fibroadenomas, or carcinomas. The multiresolution shape features are compared with traditional uniresolution shape features for their class discriminating abilities. The study involves 60 digitized mammographic images. The lesions are segmented prior to introduction to the classification system. The uniresolution and multiresolution shape features are calculated using the radial distance measure of the lesion boundaries. The discriminating power of the shape features are analyzed via linear discriminant analysis. The classification system utilizes a simple Euclidean distance measure to determine class membership. The system is tested using the apparent and leave-one-out test methods. The results of the classification system when using the multiresolution and uniresolution shape features are classification rates of 83% and 80% for the apparent and leave-one-out test methods, respectively. These results are compared with those of the system when using only the uniresolution shape features. The uniresolution classification rates are 72% and 68% for the apparent and leave-one-out test methods, respectively. Keywords: wavelet transform, multiresolution analysis, feature extraction, classification, shape, mammography, image processing