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We present a novel methodological framework for leveraging multiple image sources, including different modalities, acquisition protocols or image features, in the registration of more than two images via information theoretic data fusion. The technique, referred to as multi-attribute combined mutual information (MACMI), adopts a multivariate application of mutual information (MI) to allow several...
Three-dimensional (3D) morphometric features of anatomical objects may provide important information regarding disease outcome. In this paper we develop an integrated framework to quantitatively extract and analyze 3D surface morphology of anatomical organs. We consider two datasets: (a) synthetic dataset comprising 640 super quadratic ellipsoids, and (b) clinical dataset comprising 36 prostate MRI...
Computer-aided prognosis (CAP) is a new and exciting complement to the field of computer-aided diagnosis (CAD) and involves developing computerized image analysis and multi-modal data fusion algorithms for helping physicians predict disease outcome and patient survival. At the Laboratory for Computational Imaging and Bioinformatics (LCIB) at Rutgers University we have been developing computerized...
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