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Mammogram images are now increasingly acquired with full-field digital mammography (FFDM) systems in the clinics. Traditionally, the “for-processing” format of FFDM images is used in computer-aided diagnosis (CAD) of breast cancer. In this study, we investigate the feasibility of using “for-presentation” format of FFDM (which are more readily available) in development of CAD algorithms for microcalcification...
In diagnostic imaging, recent studies have shown that retrieval of cases that are similar to the case being evaluated can boost its classification performance. In this work we investigate how to improve the utility of the retrieved cases by considering the similarity both in the image features and in the pathology when comparing the cases. To demonstrate the benefit of this retrieval strategy, we...
Linear structures are a major contributor to false-positives (FPs) in detection of clustered microcalcifications (MCs) in mammograms. We propose a unified classifier approach to incorporate the dichotomous effect of linear structures in MC detection, the purpose being to suppress the FPs associated with linear structures. We introduce a dummy variable in the classifier model as in traditional regression...
Linear structures are a major source of false positives (FPs) in computer-aided detection of clustered microcalcifications (MCs) in mammograms. In this work, we investigate whether it is feasible to improve the performance in MC detection by directly exploiting the FPs associated with linear structures. We analyze the cause of FPs by linear structures and their characteristics with an SVM detector,...
Accurate diagnosis of microcalcification (MC) lesions in mammograms is an important but challenging clinical task in early cancer detection. In this work, we investigate how to extract salient and robust quantitative features for discriminating between benign and malignant cases in the presence of inaccuracy in MC detection. We propose to use a spatial density function (SDF) to characterize the spatial...
This work aims to explore whether we can improve the accuracy of an SVM classifier for microcalcification (MC) detection by incorporating prior knowledge of MCs in mammograms. Based on the fact that MCs are inherently invariant to their spatial orientation in a mammogram, we consider two different techniques for incorporating rotation invariance into SVM, of which one is virtual support vector SVM...
In previous work we developed a support vector machine (SVM) approach for detection of microcalcifications (MCs) in mammogram images, which was demonstrated to outperform several existing methods for MC detection in the literature. In this work, we explore whether we can further improve the performance of the SVM detector by exploiting the fact that MCs are inherently invariant to their spatial orientation...
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