February 22, 2008


Speaker: Jung-Ha An

Title: Generating Prior Shapes and Simultaneous Image Segmentation and Registration with an Application to Medical Imaging

Abstract: Generating prior shapes and image segmentation and registration are important but challenging research in medical imaging.  In  medical applications, prior shape plays a key role in obtaining accurate result in image segmentation.  A new algorithm for generating prior shape models using a Self-Organizing map is presented.  The aim of the model is to develop an approach for shape representation and classification to detect differences in the shape of anatomical structures.   The presented algorithm is applied to the eighty-five two-chamber normal human heart ultrasound end-diastolic cardiac borders.  The experimental results show the effectiveness of the algorithm in generating shape representation and classification of given various human heart cardiac borders.  For a simultaneous image segmentation and non-rigid registration, a new variational partial differential equation edge based level set method using prior shape and intensity information is presented. The segmentation is obtained by finding a non-rigid registration to the prior shape. The non-rigid registration consists of both a global rigid transformation and a local non-rigid deformation.  In this model, a prior shape is used as an initial contour which leads to decrease the numerical calculation time.  The models is tested against two chamber end systolic ultrasound images from thirteen human patients.  The experimental results provide preliminary evidence of the effectiveness of the model in detecting the boundaries of the incompletely resolved objects which were plagued by noise, dropout, and artifact.