Prostate Cancer Is One Of The Major Causes Of Cancer Death For Men In The Western World. Magnetic Resonance Imaging (MRI) Is Being Increasingly Used As A Modality To Detect Prostate Cancer. Therefore, Computer-aided Detection Of Prostate Cancer In MRI Images Has Become An Active Area Of Research. In This Paper We Investigate A Fully Automated Computer-aided Detection System Which Consists Of Two Stages. In The First Stage, We Detect Initial Candidates Using Multi-atlas-based Prostate Segmentation, Voxel Feature Extraction, Classification And Local Maxima Detection. The Second Stage Segments The Candidate Regions And Using Classification We Obtain Cancer Likelihoods For Each Candidate. Features Represent Pharmacokinetic Behavior, Symmetry And Appearance, Among Others. Furthermore, The System Has Potential In A First-reader Setting.
