Prostate Cancer Localization Using Multiparametric MRI based on Semisupervised Techniques With Automated Seed Initialization

Abstract

In this paper, we propose a novel and efficient semisupervised technique for automated prostate cancer localization using multiparametric magnetic resonance imaging (MRI). This method can be used in guiding biopsy, surgery, and therapy. We systematically present a new segmentation technique by developing a multiparametric graph based random walker (RW) algorithm with automated seed initialization to perform prostate cancer segmentation using multiparametric MRI. RW algorithm has proved to be accurate and fast in segmentation applications; however it requires a set of (user provided) seed points in order to perform segmentation. In this study, we first developed a novel RW method, which can be used with multiparametric MR images and then devised alternative methods that can determine seed points in an automated manner using discriminative classifiers such as support vector machines (SVM). Proposed RW method with automated seed initialization is able to produce improved segmentation results by assigning more weights to the images with more discriminative power.We applied the proposed method to a multiparametric dataset obtained from biopsy confirmed prostate cancer patients. Proposed method produces a sensitivity/ specificity rate of 0.76 and 0.86, respectively. Both visual, quantitative as well as statistical results are presented to show the significant performance improvements. Fisher sign test is used to demonstrate the statistical significance of our results by achieving p-values less than 0.05. This method outperforms available RW and SVM based methods by achieving a high specificity rate while not reducing sensitivity.

Computer‐aided diagnosis: detection and localization of prostate cancer within the peripheral zone

“…On the other hand, we used median filtering to preserve the regional boundaries (e.g., tumor regions). It is claimed that using median filtering is much better at preserving sharp edges , and in our case, we want to preserve the information‐bearing structures such as tumor boundaries . The median filter works by replacing the pixel value with the median value in the neighborhood of that pixel.…”

“…A recent study concluded that an abnormal DRE had 0.44 sensitivity and 0.68 specificity . On the other hand, although TRUS enables the accurate determination of prostate size and depicts zonal anatomy, its ability to detect cancer tissue is limited with sensitivity and specificity varying between 40% and 50% .…”

“…Note that the transitional zone is located within the CZ but no definable boundary between these regions is expected on MRI. The ultimate goal of this research is to develop a CAD tool for prostate cancer detection and localization within the PZ mainly because (a) about 80% of the prostate cancers appear in the PZ and (b) in general, prostate cancer that arises in the PZ is more aggressive than that that arises in the transitional or CZs . Therefore, in this paper, we propose a new method for detecting prostate abnormality within the PZ using four different image features (Figure ) extracted using Gaussian and median filters.…”

Computer-aided diagnosis method for MRI-guided prostate biopsy within the peripheral zone using grey level histograms

“…We used median filter because it is better at preserving regional boundaries (e.g. tumor regions) and sharp edges [22]. In our case we want to preserve the information-bearing structures such as tumor's edge boundaries [22].…”

2012 Index IEEE Transactions on Information Technology in Biomedicine Vol. 16

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