A new framework for image based physiological cardiac monitoring is proposed based on repeated imaging of critical slice locations in an interventional MRI environment. The aim of this work is to provide a method of detecting pathological changes in the left ventricular (LV) myocardial wall motion where the standard ECG methods are not possible due to distortions by the magnetic field. First MRI LV short axis images are acquired for different phases of the cardiac cycle over RR intervals. Then LV contours are detected based on an established segmentation algorithm. The contour's Fourier Descriptors are calculated to classify myocardial wall into two classes: contracted or not contracted. The classifier is trained during an initial observation period before a pathological change might occur during an intervention. A contour rejected by the classifier using the unconditional, predictive probability of the contour's observation vector as confidence measure is interpreted as a probably pathologic change in the LV myocardial wall motion. To evaluate the performance of the classifier a simple model is introduced for simulating the contours of a pathological, ischemic, LV myocardial wall. The overall performance of the classifier on 516 samples based on healthy volunteer images and 3096 simulated ischemic samples yielded a mean classification error for supervised training of 5.7% and for unsupervised training of 8.7%.