This blog presents a novel non-invasive approach for early diagnosis of prostate cancer from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). In order to precisely analyze the complex 3D DCE-MRI of the prostate, a novel processing frame work that consists of four main steps is proposed. The first step is to isolate the prostate from the surrounding anatomical structures based on a Maximum a Posteriori (MAP) estimate of a log likelihood function that accounts for the shape priori, the spatial interaction, and the current appearance of the prostate tissues and its background (surrounding anatomical structures). In the second step, a non-rigid registration algorithm is employed to account for any local deformation that could occur in the prostate during the scanning process due to the patient’s breathing and local motion. In the third step, the perfusion curves that show propagation of the contrast agent into the tissue are obtained from the se
Interested in pattern recognition and machine learning.