AN EFFICIENT TISSUE CLASSIFIER FOR BUILDING PATIENT-SPECIFIC FINITE-ELEMENT MODELS FROM X-RAY CT IMAGES

We developed an efficient semiautomatic tissue classifier for X-ray co mputed tomography (CT) images which can be used to build patient- or a nimal-specific finite element (FE) models for bioelectric studies. The classifier uses a gray scale histogram for each tissue type and three -dimensional (3-D) neighborhood information. A total of 537 CT images from four animals (pigs) were classified with an average accuracy of 9 6.5% compared to manual classification by a radiologist. The use of 3- D, as opposed to 2-D, information reduced the error rate by 78%. Model s generated using minimal or full manual editing yielded substantially identical voltage profiles. For the purpose of calculating voltage gr adients or current densities in specific tissues, such as the myocardi um, the appropriate slices need to be fully edited, however. Our class ifier offers an approach to building FE models from image information with a level of manual effort that can be adjusted to the need of the application.