This work presents a fully-automated framework for the pre-processing of free-breathing myocardial perfusion MRI data. Image series are first split into low-rank and sparse components using RPCA. This allows estimation of the deformation fields required to motion correct the image series, in the absence of dynamic contrast enhancement. Once motion corrected, pixels are clustered into anatomically relevant clusters using perfusion-superpixels which groups nearby pixels that have similar time dynamics. A LDA classifier is trained which allows the generation of myocardial probability maps and active contours are fit to the high probability regions to give a delineation of the myocardium.