Predictive deconvolution and hybrid feature selection for computer-aided detection of prostate cancer.
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.
Maggio
Simona
S
Department of Electronics, Computer Science, and Systems (DEIS) and E. De Castro Advanced Research Center on Electronic Systems (ARCES), University of Bologna, 40136 Bologna, Italy. simona.maggio@unibo.it
Palladini
Alessandro
A
Marchi
Luca De
LD
Alessandrini
Martino
M
Speciale
Nicolò
N
Masetti
Guido
G
D000368:Aged; D000465:Algorithms; D016002:Discriminant Analysis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D012372:ROC Curve; D014463:Ultrasonography / Q000379:methods*