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 computed tomography (CT) images which can be used to build patient- or animal-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 96.5% compared to manual classification by a radiologist. The use of 3-D, as opposed to 2-D, information reduced the error rate by 78%. Models generated using minimal or full manual editing yielded substantially identical voltage profiles. For the purpose of calculating voltage gradients or current densities in specific tissues, such as the myocardium, the appropriate slices need to be fully edited, however. Our classifier 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.
Shrinidhi
N
N
Department of Electrical Engineering, University of Washington, Seattle 98195, USA.
Haynor
D R
DR
Wang
Y
Y
Jorgenson
D B
DB
Bardy
G H
GH
Kim
Y
Y
D000465:Algorithms; D000818:Animals; D006801:Humans; D008954:Models, Biological; D009928:Organ Specificity; D015203:Reproducibility of Results; D013045:Species Specificity; D013552:Swine; D013997:Time Factors; D014057:Tomography, X-Ray Computed / Q000145:classification* / Q000379:methods / Q000706:statistics & numerical data