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Article summary:

1. MRI tissue segmentation and classification is challenging due to a lack of signal intensity standardization.

2. The proposed method integrates quantitative MRI values (T1 and T2 relaxation times) and machine learning (Support Vector Machine) to segment and classify pelvic tissues.

3. The SVM classification accuracy was excellent for prostate, fat, muscle, bone marrow, and bladder, but not always successful for air within the rectum due to lack of training data.

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