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A comparative technique and performance results on novel learned snakes in two dissimilar medical domains.
| Content Provider | CiteSeerX |
|---|---|
| Author | Fenster, Samuel D. Kender, John R. |
| Abstract | We review our work on how to teach deformable models to maximize image segmentation correctness based on user-specified criteria. We then present new variants and applications of learned snakes, modeled by four different probability density functions (PDFs), at three scales, and in the two very different medical domains of abdominal CT slices and echocardiograms. We review and extend our method for evaluating which criteria work best. Success depends on the relation of objective function (the PDF) output to shape correctness. This relationship, for all the above learned snake variants and domains, is evaluated on perturbed ground truth shapes in three ways: by the incidence of "false positives" (scoring better than ground truth) of randomized shapes; by the monotonicity of the objective function versus shape closeness to ground truth, as given by a correlation coefficient; and by the distance of this relationship to the nearest monotonically increasing function, a new performance meas... |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Novel Learned Snake Dissimilar Medical Domain Comparative Technique Performance Result Ground Truth Objective Function Different Medical Domain Randomized Shape False Positive New Performance Meas Correlation Coefficient User-specified Criterion Image Segmentation Correctness Perturbed Ground Truth Shape Abdominal Ct Slice Shape Closeness New Variant Snake Variant Deformable Model Learned Snake Different Probability Density Function |
| Content Type | Text |
