Jan. 16, 2014 from 2 p.m. to 3 p.m.
- Allen Tannenbaum
Title: Novel Methods in Medical Image Computation
In this talk, we will describe the use of various modern mathematical techniques for several key problems in medical imaging. First we will describe the use of various geometric curvature based flows for fast reliable segmentation of medical imagery both in 2D and 3D. This will lead to modern approaches to the topic of geometric active contours.
The term ``active contours'' does not refer to one specific technique, but rather to a broad family of related methods that can be tailored to a specific image processing task. Both local (edge-based) and global (statistics-based) information may be included in this framework. The underlying principle is based on the use of deformable contours that conform to various object shapes and motions. Active contours are used for edge detection, image segmentation, shape modeling, and for tracking. Further we will give some directional active contour models for extracting white matter fiber tracts in Diffusion Weighted Magnetic Resonance (MR) imagery. Related flows for smoothing and enhancement will also be considered. These flows have certain interesting invariance properties that can be tuned for several important tasks in general image processing and computer vision. We will provide some of the relevant results from the theory of curve and surface evolution in order to motivate our methods.
Next, we will outline some recent work using conformal mappings for the visualization of the cortical brain surface and automatic polyp detection in virtual colonoscopy. Regarding the former problem, we show how the method may be used in functional MR imagery to better visualize brain activity. Regarding the latter problem, we demonstrate how the conformal flattening approach leads to a surface scan of the entire colon as a cine, and affords viewer the opportunity to examine each point on the surface without distortion. Both flattening tasks (brain and colon) employ the theory angle-preserving mappings from differential geometry in order to derive an explicit method for surface flattening. Indeed, we describe a general method based on a discretization of the Laplace-Beltrami operator for flattening a surface in a manner that preserves the local geometry. From a triangulated surface representation of the surface, we indicate how the procedure may be implemented using a finite element technique, which takes into account special boundary conditions.
The talk is designed to be accessible to a general audience with an interest in medical imaging. We will demonstrate our techniques with a wide variety of medical
Professor of Computer Science and Applied Mathematics