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According to the data of World Cancer Research Fund International prostate cancer is the second most common after lung cancer and the fifth most common cause of cancer death amongst men. Prostate cancer is also the fourth most frequent tumor between both genders worldwide. Biopsy is the only way to detect prostate cancer so far. Statistics show that it is able to detect only 70-80% of clinically significant cancer cases. Multi parametric magnetic resonance imaging technique comes to play to help in determining the location to perform biopsy on. The first step to automating the detection of the location is applying prostate segmentation on magnetic resonance images. The fact that there is lack of standardization of signal intensity to acquire those images burdens the problem of automated prostate segmentation. Authors review the results of Prostate MR Image Segmentation (PROMISE12) challenge, designed to evaluate and compare different prostate segmentation algorithms, and provide insights on automated prostate segmentation by applying best open source algorithm under different circumstances. Authors applied selected algorithm on two different datasets and showed how segmentation results can be improved by applying even the most primitive image stretching techniques. Authors also showed that algorithm is promising in segmenting unseen dataset.