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Robust adaptive segmentation of 3D medical images with level sets (2000)
| Content Provider | CiteSeerX |
|---|---|
| Author | Baillard, C. Barillot, C. Bouthemy, P. |
| Abstract | : This paper is concerned with the use of the Level Set formalism to segment anatomical structures in 3D medical images (ultrasound or magnetic resonance images). A closed 3D surface propagates towards the desired boundaries through the iterative evolution of a 4D implicit function. The major contribution of this work is the design of a robust evolution model based on adaptive parameters depending on the data. First the step size and the external propagation force factor, both usually predetermined constants, are automatically computed at each iteration. Additionally, regionbased information, rather than spatial image gradient, is exploited by estimating intensity probability density functions over the image. As a result, the method can be applied to various kinds of data. Quantitative and qualitative results on brain MR images and 3D echographies of carotid arteries are reported and discussed. Key-words: 3D segmentation, deformable models, level sets, intensity distribution, brain MR... |
| File Format | |
| Language | English |
| Publisher Date | 2000-01-01 |
| Publisher Institution | Research Report 1369, IRISA (Rennes Cedex |
| Access Restriction | Open |
| Subject Keyword | Medical Image Level Set Robust Adaptive Segmentation Iterative Evolution Desired Boundary Qualitative Result Brain Mr Deformable Model Intensity Distribution Brain Mr Image External Propagation Force Factor Surface Propagates Level Set Formalism Spatial Image Gradient Major Contribution Regionbased Information Step Size Segment Anatomical Structure Implicit Function Intensity Probability Density Function Magnetic Resonance Image Various Kind Carotid Artery Adaptive Parameter Robust Evolution Model |
| Content Type | Text |
| Resource Type | Technical Report |
