Journal article
Object localization and border detection criteria design in edge-based image segmentation : Automated learning from examples
IEEE transactions on medical imaging, Vol.19(10), pp.973-985
2000
DOI: 10.1109/42.887613
PMID: 11131495
Abstract
This paper provides methodology for fully automated model-based image segmentation. All information necessary to perform image segmentation is automatically derived from a training set that is presented in a form of segmentation examples. The training set is used to construct two models representing the objects--shape model and border appearance model. A two-step approach to image segmentation is reported. In the first step, an approximate location of the object of interest is determined. In the second step, accurate border segmentation is performed. The shape-variant Hough transform method was developed that provides robust object localization automatically. It finds objects of arbitrary shape, rotation, or scaling and can handle object variability. The border appearance model was developed to automatically design cost functions that can be used in the segmentation criteria of edge-based segmentation methods. Our method was tested in five different segmentation tasks that included 489 objects to be segmented. The final segmentation was compared to manually defined borders with good results [rms errors in pixels: 1.2 (cerebellum), 1.1 (corpus callosum), 1.5 (vertebrae), 1.4 (epicardial), and 1.6 (endocardial) borders]. Two major problems of the state-of-the-art edge-based image segmentation algorithms were addressed: strong dependency on a close-to-target initialization, and necessity for manual redesign of segmentation criteria whenever new segmentation problem is encountered.
Details
- Title: Subtitle
- Object localization and border detection criteria design in edge-based image segmentation : Automated learning from examples
- Creators
- Marek BREJL - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, United StatesMilan SONKA - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, United States
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on medical imaging, Vol.19(10), pp.973-985
- DOI
- 10.1109/42.887613
- PMID
- 11131495
- NLM abbreviation
- IEEE Trans Med Imaging
- ISSN
- 0278-0062
- eISSN
- 1558-254X
- Publisher
- Institute of Electrical and Electronics Engineers; New York, NY
- Language
- English
- Date published
- 2000
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984046904602771
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