MRI IMAGE REGISTRATION BASED SEGMENTATION FRAMEWORK FOR WHOLE HEART

Abstract

Magnetic resonance (MR) imaging has proven successful and very important in determining patient’s internal morphology. Segmentation of whole heart helps in providing important morphological information of the heart, that is helpful in various clinical applications. To avoid inter-and intra-observer variations of manual delineation, it's extremely necessary to implement a technique for automatic segmentation of the full heart. However automating process is complicated by the large shape variation of the heart and limited quality of the data. The main aim of this framework is to develop automatic and robust segmentation framework from cardiac MRI while overcoming these difficulties. This paper, introduces a registration framework ready to preserve the topology and to alter the big form variability of the heart. The core of our framework relies on two contributions extending this segmentation-propagation frameworks, namely a locally Affine Registration technique (LARM) a new algorithm has been proposed for inverting the transformation based on Dynamic re-sampling And distance Weighting interpolation (DRAW) and a non-rigid registration i.e. free-form deformations with adaptive management of the status of every point(ACPS FFDs) which achieves one to one mapping . Thus the Registered cardiac image can be further segmented to get the optimized results.