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.
Predictive Deconvolution and Hybrid Feature Selection for Computer-Aided Detection of Prostate Cancer / S. Maggio; A. Palladini; L. De Marchi; M. Alessandrini; N. Speciale; G. Masetti. - In: IEEE TRANSACTIONS ON MEDICAL IMAGING. - ISSN 0278-0062. - STAMPA. - 29:(2010), pp. 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.