A New Medical Image Analysis Approach Integrating Expert Systems and Deformable Models

Henry Ford Hospital, Detroit MI

Investigators

Abstract

9911084 SoltanianZadeh In recent years, deformable models have been proposed for a variety of image analysis applications in industry, medicine, and computer vision. However, there does not exist a reliable automated method for defining the initial shape of the model for many of the medical image analysis applications, e.g., those dealing with structures with significant morphological variations. In addition, most of the deformable models used in these applications are two dimensional (2D), i.e., do not use the 3D structure of the data, and do not generate accurate results when the desired structure does not have an edge in part of the image. Finally, they do not utilize multi-parametric gray level information to characterize the desired structure. The goal of this project is to develop a new approach, using an expert system and a 3D deformable model, to overcome the above difficulties. Developments will be done in the context of an important biomedical application, and will localize, segment, and characterize the hippocampus from magnetic resonance imaging (MRI) studies of the brain. The methods will be tested, evaluated, and validated, using simulated images and clinical studies of epileptic patients. Electroencephalographic (EEG) results and postsurgery outcomes will be used as ``gold standards'' for evaluation and validation of the image analysis methods. The proposed research will be a break through in the development and application of deformable models, and will advance image analysis science in the direction of integrating expert systems and deformable models. To illustrate practical use of the new methods, they will be used for establishing more specific and sensitive means of identifying foci of epileptogenicity from MRI. This will allow for a more contracted EEG investigation by focusing attention into the abnormal site. The end result will be reductions in diagnostic cost, painful investigational operations, and lengthy hospital stays for a majority of patients. The proposed approach is applicable to the identification, segmentation, and characterization of other brain structures (e.g., ventricles, amygdala, red nucleus, substantia nigra, globus pallidus, putamen, corpus callosum). It is also applicable to virtually any image analysis task for which deformable models are used.