  We consider the problem of automatically prescribing oblique planes (short
axis, 4 chamber and 2 chamber views) in Cardiac Magnetic Resonance Imaging
(MRI). A concern with technologist-driven acquisitions of these planes is the
quality and time taken for the total examination. We propose an automated
solution incorporating anatomical features external to the cardiac region. The
solution uses support vector machine regression models wherein complexity and
feature selection are optimized using multi-objective genetic algorithms.
Additionally, we examine the robustness of our approach by training our models
on images with additive Rician-Gaussian mixtures at varying Signal to Noise
(SNR) levels. Our approach has shown promising results, with an angular
deviation of less than 15 degrees on 90% cases across oblique planes, measured
in terms of average 6-fold cross validation performance -- this is generally
within acceptable bounds of variation as specified by clinicians.
