Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation

Presented at CI2BM09 - MICCAI Workshop on Cardiovascular Interventional Imaging and Biophysical Modelling, London, UK, September 20, 2009.The study of the coronary vessel structure is crucial to the diagnosis of atherosclerosis and other cardiovascular diseases, which together account for about 35% of all deaths in the United States per year. Vessel Segmentation from CTA data is challenging because of non-uniform image intensity along the vessel, and the branching and thinning geometry of the vessel tree. We present a novel method for vessel extraction that models the vasculature as a tubular tree and individual vessels as 3D tubes. We create an initial tube from a few seed points within the vessel tree, and then evolve this initial tube using a variational energy optimization approach to capture the vessel while automatically detecting branches in the vessel tree. A significant advantage of our proposed framework is that the center-line of the blood vessel tree, which is useful in defining cross sectional area of the vessel and evaluating stenoses, is detected automatically as the tubular tree evolves. Existing approaches on the other hand need an explicit step for skeletonization of the vessel volume after segmentation. Another benefit is that the parent-child relationships between branches are also automatically obtained, which is useful in fly-through visualization as well as clinical reporting

Statistical models in medical image analysis

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (leaves 149-156).Computational tools for medical image analysis help clinicians diagnose, treat, monitor changes, and plan and execute procedures more safely and effectively. Two fundamental problems in analyzing medical imagery are registration, which brings two or more datasets into correspondence, and segmentation, which localizes the anatomical structures in an image. The noise and artifacts present in the scans, combined with the complexity and variability of patient anatomy, limit the effectiveness of simple image processing routines. Statistical models provide application-specific context to the problem by incorporating information derived from a training set consisting of instances of the problem along with the solution. In this thesis, we explore the benefits of statistical models for medical image registration and segmentation. We present a technique for computing the rigid registration of pairs of medical images of the same patient. The method models the expected joint intensity distribution of two images when correctly aligned. The registration of a novel set of images is performed by maximizing the log likelihood of the transformation, given the joint intensity model. Results aligning SPGR and dual-echo magnetic resonance scans demonstrate sub-voxel accuracy and large region of convergence. A novel segmentation method is presented that incorporates prior statistical models of intensity, local curvature, and global shape to direct the segmentation toward a likely outcome. Existing segmentation algorithms generally fit into one of the following three categories: boundary localization, voxel classification, and atlas matching, each with different strengths and weaknesses. Our algorithm unifies these approaches. A higher dimensional surface is evolved based on local and global priors such that the zero level set converges on the object boundary. Results segmenting images of the corpus callosum, knee, and spine illustrate the strength and diversity of this approach.by Michael Emmanuel Leventon.Ph.D

Trends in Mathematical Imaging and Surface Processing

Motivated both by industrial applications and the challenge of new problems, one observes an increasing interest in the field of image and surface processing over the last years. It has become clear that even though the applications areas differ significantly the methodological overlap is enormous. Even if contributions to the field come from almost any discipline in mathematics, a major role is played by partial differential equations and in particular by geometric and variational modeling and by their numerical counterparts. The aim of the workshop was to gather a group of leading experts coming from mathematics, engineering and computer graphics to cover the main developments