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We formulate the problem of microcalcification detection in digital mammograms as a statistical change detection problem in the local properties of the image. First, we represent mammograms by two-dimensional autoregressive moving-average (2D ARMA) fields; thus uniquely characterizing the images by their reduced dimensionality 2D ARMA feature vectors. Texture changes in mammograms are then modeled...
In the analysis of digital or digitized mammographic images, a requirement is to learn to separate benign abnormalities from malignant ones. Such an activity could form part of a computer-aided diagnosis (CAD) tool. We present a CAD study of mass and calcification lesions found in digital database of screening mammography (DDSM) using BI-RADS-based features to demonstrate the performance of feature...
The purpose of this work is to compare the performance of support vector machines (SVM) and multi-layer perceptron (MLP) in the task of detection and diagnosis of microcalcification clusters in mammograms (MCCs). As data source, the "digital database for screening mammography"; (DDSM) was used. The results show a similar performance for SVM and MLP, in both tasks, detection and diagnosis...
The purpose of this study is to develop and evaluate a probabilistic framework for reliability analysis of information-theoretic computer-assisted detection (IT-CAD) systems in mammography. The study builds upon our previous work on a feature-based reliability analysis technique tailored to traditional CAD systems developed with a supervised learning scheme. The present study proposes a probabilistic...
The purpose of this study is to develop and evaluate a probabilistic framework for reliability analysis of information-theoretic computer-assisted detection (IT-CAD) systems in mammography. The study builds upon our previous work on a feature-based reliability analysis technique tailored to traditional CAD systems developed with a supervised learning scheme. The present study proposes a probabilistic...
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