Analysis of mammogram classification using a wavelet transform decomposition
Pattern Recognition Letters - Special issue: Sibgrapi 2001
An evaluation of wavelet features subsets for mammogram classification
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Pectoral muscle segmentation: A review
Computer Methods and Programs in Biomedicine
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The paper presents a new scheme for analysis of linear directional components in images by using a multiresolution representation based on Gabor wavelets. A dictionary of Gabor filters with varying tuning frequency and orientation, specifically designed in order to reduce the redundancy in the wavelet-based representation, is applied to the given image. The filter responses for different scales and orientation are analyzed by using the Karhunen-Loeve (KL) transform and Otsu's (1979) method of thresholding. The KL transform is applied to select the principal components of the filter responses, preserving only the most relevant directional elements appearing at all scales. The first N principal components, thresholded by using Otsu's method, are used to reconstruct the magnitude and phase of the directional components of the image. Rose diagrams computed from the phase images are used for quantitative and qualitative analysis of the oriented patterns. The proposed scheme is applied to the analysis of asymmetry between left and right mammograms. For this purpose, a set of three features is extracted from the Rose diagrams and used in a parametric statistical classifier. A total of 80 images from 20 normal cases, 14 asymmetric cases, and 6 distortion cases from the Mini-MIAS database were used to evaluate the scheme using the leave-one-out methodology, resulting in an average diagnostic accuracy of 72.5%.