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.
S. Maggio, A. Palladini, L. De Marchi, M. Alessandrini, N. Speciale, G. Masetti (2010). Predictive Deconvolution and Hybrid Feature Selection for Computer-Aided Detection of Prostate Cancer. IEEE TRANSACTIONS ON MEDICAL IMAGING, 29, 455-464 [10.1109/TMI.2009.2034517].
Predictive Deconvolution and Hybrid Feature Selection for Computer-Aided Detection of Prostate Cancer
MAGGIO, SIMONA;PALLADINI, ALESSANDRO;DE MARCHI, LUCA;ALESSANDRINI, MARTINO;SPECIALE, NICOLO'ATTILIO;MASETTI, GUIDO
2010
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.