Comparison between K mean and fuzzy C-mean methods for segmentation of near infrared fluorescent image for diagnosing prostate cancer

In each year there are thousands of people die due to prostate cancer. Near-infrared (NIRF) optical imaging is a new technique that uses the high absorption of hemoglobin in prostate´s cancer cells for early detection. We use Image segmentation method to segment and extract the cancer region in the prostate´s infrared images. In this paper, two image segmentation methods: K-means algorithm and fuzzy c-means (FCM) algorithms are discussed and compared. The extracted cancer clusters by two algorithms are compared using Student t-test and we found that the K-mean is more accurate approach than FCM in extracting the exact shape of tumors.