Computer-aided detection of prostate cancer in T2-weighted MRI within the peripheral zone.
In this paper we propose a prostate cancer computer-aided diagnosis (CAD) system and suggest a set of discriminant texture descriptors extracted from T2-weighted MRI data which can be used as a good basis for a multimodality system. For this purpose, 215 texture descriptors were extracted and eleven different classifiers were employed to achieve the best possible results. The proposed method was tested based on 418 T2-weighted MR images taken from 45 patients and evaluated using 9-fold cross validation with five patients in each fold. The results demonstrated comparable results to existing CAD systems using multimodality MRI. We achieved an area under the receiver operating curve (A z ) values equal to [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] for Bayesian networks, ADTree, random forest and multilayer perceptron classifiers, respectively, while a meta-voting classifier using average probability as a combination rule achieved [Formula: see text].
Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK.
Andrik
A
Rampun
Ling
L
Zheng
Paul
P
Malcolm
Bernie
B
Tiddeman
Reyer
R
Zwiggelaar
D000465:Algorithms; D001499:Bayes Theorem; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D011336:Probability; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D012372:ROC Curve