Registration and segmentation methodology for MR image analysis : Application to cardiac and renal images

Abstract

Magnetic resonance imaging (MRI) has emerged as a reliable tool for functional analysis of internal organs like the kidney and the heart. Due to the considerable length of time taken to acquire MR images, they are affected by patient motion. Besides, MR images are characterized by low spatial resolution, noise and rapidly changing intensity. Rapid intensity change is the primary challenge that MR image registration methods need to address. In this thesis, first we investigate a saliency based method for rigid registration of renal perfusion images. A neurobiology based visual saliency model is used for this purpose. Saliency acts as a contrast invariant metric, and a mutual information framework is used. The second part of our work deals with elastic registration of cardiac perfusion images. The saliency model is modified to reflect the local similarity property at every pixel. Markov random fields (MRFs) were used to integrate saliency and gradient information for elastic registration. Apart from being a contrast invariant metric, saliency also influences the smoothness of the registration field and speeds up registration by identifying pixels relevant for registration. In the final part of our work we investigate a joint registration and segmentation (JRS) method for the perfusion images. JRS is particularly important for MR images in order to fully exploit the available temporal information from the image sequence. MRFs were used to combine the mutual dependency of registration and segmentation information by formulating the data penalty and smoothness cost as a function of registration and segmentation labels. The displacement vector and segmentation class of every pixel was obtained from multi-resolution graph cut optimization. This eliminates the need for a separate segmentation step and also increases computation speed. Experimental results show that our works improve upon current techniques that solve registration and segmentation separately. Future work will involve making our proposed JRS method robust to different datasets, and investigating the possibility of using learning techniques to solve the registration and segmentation problem.