A Piecewise Smooth Image Segmentation Using Gamma-Convergence Approximation in Medical Imaging

Applied and Computational Mathematics Seminar
Monday, April 18, 2011 - 14:00
1 hour (actually 50 minutes)
Skiles 005
California State University, Stanislaus
Medical imaging is the application of mathematical and engineering models to create images of the human body for clinical purposes or medical science by using a medical device. One of the main objectives of medical imaging research is to find the boundary of the region of the interest. The procedure to find the boundary of the region of the interest is called a segmentation. The purpose of this talk is to present a variational region based algorithm that is able to deal with spatial perturbations of the image intensity directly. Image segmentation is obtained by using a Gamma-Convergence approximation for a multi-scale piecewise smooth model. This model overcomes  the limitations of global region models while avoiding the high sensitivity of local approaches. The proposed model is implemented efficiently using recursive Gaussian convolutions. The model is applied to magnetic resonance (MR) images where image quality  depends highly on the acquisition protocol. Numerical experiments on 2-dimensional human liver  MR images show that our model compares favorably to existing methods.This work is done in collaborated with Mikael Rousson and Chenyang Xu.