Classifying mammographic mass shapes using the wavelet transform modulus-maxima method

Abstract

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.

Classification of Breast Masses based on Cognitive Resonance

“…Zernike moments [3], [4], [14] need segmentation, co-scaling using NRL vector and translation. NRL features also need segmentation [5], [15]. Eventually, SpI feature [16] needs segmentation, co-scaling using NRL vector, translation and histogram equalization.…”

“…Circularity [9] and Zernike moments [3], [4], [14] are proper descriptors of mass shape. The NRL derivatives [5], [15] and SpI [16] are appropriate descriptors of mass margin. Moreover, contrast and average gray level [9] are suitable descriptors of mass density.…”

Fourier Transform Based Early Detection of Breast Cancer by Mammogram Image Processing

“…The other limitations of mammography are associated with irregular shapes and locations of cancerous masses in the breast. There are different shapes that have been identified and published in the literature [4] as shown in Figure 1. It is already established that a well-defined round shape masses are considered benign and an irregular shape masses are considered malignant [4].…”

“…There are different shapes that have been identified and published in the literature [4] as shown in Figure 1. It is already established that a well-defined round shape masses are considered benign and an irregular shape masses are considered malignant [4]. Sometimes the masses are hidden in the breast tissue and hence they are difficult to trace.…”

Soft Computing Based Decision Making Approach for Tumor Mass Identification in Mammogram

“…al. [17] reported the results of applying multiresolution techniques to the problem of tumor mass classification. They utilized discrete wavelet transform modulus-maxima method for the extraction of mammographic mass shape features.…”