Detection of prostate abnormality within the peripheral zone using local peak information

In this paper, a fully automatic method is proposed for the detection of prostate cancer within the peripheral zone. The method starts by filtering noise in the original image followed by feature extraction and smoothing which is based on the Discrete Cosine Transform. Next, we identify the peripheral zone area using a quadratic equation and divide it into left and right regions. Subsequently, peak detection is performed on both regions. Finally, we calculate the percentage similarity and Ochiai coefficients to decide whether abnormality occurs. The initial evaluation of the proposed method is based on 90 prostate MRI images from 25 patients and 82.2% (sensitivity/specificity: 0.81/0.84) of the slices were classified correctly with 8.9% false negative and false positive results.