Local Probabilistic Atlases and a Posteriori Correction for the Segmentation of Heart Images

Atlas-based segmentation is a well-known method for segmentation of medical images. In particular, this method could be used in an efficient way to automatically segment heart structures in MRI or CT scans. We propose, in this paper a more adaptive and interactive atlas-based segmentation method. The model presented combines several local probabilistic atlases with a topological graph. The local atlases provide more refined information about the structures’ shape while the spatial relationships between the atlases are learned and stored in a graph. Hence, local registrations need less computational time and the image segmentation can be guided by the user in an incremental way. Following this step, a pixel classification is performed with a hidden Markov random field that integrates the learned a priori information with the pixel intensities that originate from different modalities. Finally, an a posteriori correction is performed using Adaboost classifiers in order to correct voxels in the border of the seek region and improve the precision of the results. The proposed method is tested on CT scan and MRI images of the heart coming from the MM-WHS challenge.