Classification of mass and normal breast tissue on digital mammograms: Multiresolution texture analysis

TLDR

The wavelet transform condenses image information into coefficients, enabling efficient analysis of mammographic textures. The study examined whether multiresolution texture analysis could distinguish masses from normal breast tissue on mammograms. The authors extracted multiresolution texture features from wavelet‑decomposed ROIs of 168 mass and 504 normal parenchyma images, then used stepwise linear discriminant analysis to select optimal features for classification. Texture features at large pixel distances were important, and the linear discriminant classifier achieved AUCs of 0.89 (training) and 0.86 (test), demonstrating effective mass classification.

Abstract

We investigated the feasibility of using multiresolution texture analysis for differentiation of masses from normal breast tissue on mammograms. The wavelet transform was used to decompose regions of interest (ROIs) on digitized mammograms into several scales. Multiresolution texture features were calculated from the spatial gray level dependence matrices of (1) the original images at variable distances between the pixel pairs, (2) the wavelet coefficients at different scales, and (3) the wavelet coefficients up to certain scale and then at variable distances between the pixel pairs. In this study, 168 ROIs containing biopsy‐proven masses and 504 ROIs containing normal parenchyma were used as the data set. The mass ROIs were randomly and equally divided into training and test groups along with corresponding normal ROIs from the same film. Stepwise linear discriminant analysis was used to select optimal features from the multiresolution texture feature space to maximize the separation of mass and normal tissue for all ROIs. We found that texture features at large pixel distances are important for the classification task. The wavelet transform can effectively condense the image information into its coefficients. With texture features based on the wavelet coefficients and variable distances, the area A z under the receiver operating characteristic curve reached 0.89 and 0.86 for the training and test groups, respectively. The results demonstrate that a linear discriminant classifier using the multiresolution texture features can effectively classify masses from normal tissue on mammograms.