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Predictive Deconvolution and Hybrid Feature Selection for Computer-Aided Detection of Prostate Cancer

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6 Author(s)
Maggio, S. ; Dept. of Electron., Comput. Sci., & Syst., Univ. of Bologna, Bologna, Italy ; Palladini, A. ; De Marchi, L. ; Alessandrini, M.
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Computer-aided detection (CAD) schemes are decision making support tools, useful to overcome limitations of problematic clinical procedures. Trans-rectal ultrasound image based CAD would be extremely important to support prostate cancer diagnosis. An effective approach to realize a CAD scheme for this purpose is described in this work, employing a multi-feature kernel classification model based on generalized discriminant analysis. The mutual information of feature value and tissue pathological state is used to select features essential for tissue characterization. System-dependent effects are reduced through predictive deconvolution of the acquired radio-frequency signals. A clinical study, performed on ground truth images from biopsy findings, provides a comparison of the classification model applied before and after deconvolution, showing in the latter case a significant gain in accuracy and area under the receiver operating characteristic curve.

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Medical Imaging, IEEE Transactions on  (Volume:29 ,  Issue: 2 )