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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 segmented prostate of the whole image sequence of the patient. 

In the final step, we collect two features from these curves and use a kn-Nearest Neighbor classifier classifier to distinguish between malignant and benign detected tumors.

Moreover, in this chapter, we introduce a new approach to generate color maps that illustrate the propagation of the contrast agent in the prostate tissues based on the analysis of the 3D spatial interaction of the change of the gray level values of prostate voxels using a Generalized Gauss-Markov Random Field (GGMRF) image model.

Finally, the tumor boundaries are determined using a level set deformable model controlled by the perfusion information and the spatial interactions between the prostate voxels. Experimental results on 30 clinical DCE-MRI data sets yield promising results.


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