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We are investigating the use of texture features to distinguish abnormal from normal tissue for computer-aided diagnosis algorithms. In this study, the improvement in the accuracy of classifying masses from normal breast tissue on digitized mammograms by using local texture features was evaluated. One hundred and sixty-eight mammograms were randomly selected from patient files and digitized with a laser scanner at a pixel size of 100 micrometers . Four different regions of interest (ROIs), each of 256 X 256 pixels, were selected manually from each of the digitized mammograms. One of the four ROIs contained a biopsy-proven mass and the other three contained normal parenchyma including dense, mixed dense/fatty, and fatty tissues. The mass ROIs were randomly and equally divided into a training and a test group along with corresponding normal ROIs from the same film. The wavelet transform was used to decompose the ROIs into several scales. Global multiresolution texture features were calculated from the spatial gray level dependence matrices of the low-pass wavelet coefficients up to a certain scale and then at variable distances between the pixel pairs. Texture features and their differences in suspicious object sub-region and its peripheral sub-regions of the ROIs were also calculated to form a local texture feature space. Stepwise linear discriminant analysis was used to select effective features from the combined global-local feature space to maximize the separation of mass and normal tissue for all ROIs. Receiver operating characteristic (ROC) analysis was used to evaluate the classification accuracy and its improvement using features from global and local feature spaces. Using the global multiresolution feature space alone, it was found that the texture features at large pixel distances were important for the classification task. Using local features only, the classification accuracy was comparable to that of the global features. With the combined global and local feature spaces, the average area, Az, under the ROC curve reached 0.91 and 0.90 for the training and test groups, respectively. The improvement was statistically significant. The results demonstrate that a linear discriminant classifier using the combination of global multiresolution texture features and the local texture features can effectively classify masses from normal tissue on mammograms.
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