Journal article
Medical image segmentation: Automated design of border detection criteria from examples
Journal of electronic imaging, Vol.8(1), pp.54-64
1999
DOI: 10.1117/1.482684
Abstract
This paper provides examples of several medical image analysis applications for which single-purpose border detection approaches were developed in the past. However, the utility of these and other existing automated and semiautomated medical image analysis systems is limited by their narrow, frequently single-purpose orientation. After a general approach to graph-based optimal border detection is overviewed, a new method for design of image segmentation systems is reported, in which the criterion of optimality is automatically determined by learning from border tracing examples. Border features employed in the designed method are selected from a predefined global set using radial-basis neural networks. The method was validated in intracardiac, intravascular, and ovarian ultrasound images. The achieved performance was comparable to that of our previously reported single-purpose border detection methods. Our approach facilitates development of general multi-purpose image segmentation systems that can be trained for different types of image segmentation applications. ©
Details
- Title: Subtitle
- Medical image segmentation: Automated design of border detection criteria from examples
- Creators
- Marek Brejl - The University of Iowa, Departments of Electrical and Computer Engineering, Iowa City, Iowa 52242Milan Sonka - The University of Iowa, Departments of Electrical and Computer Engineering, Iowa City, Iowa 52242
- Resource Type
- Journal article
- Publication Details
- Journal of electronic imaging, Vol.8(1), pp.54-64
- DOI
- 10.1117/1.482684
- ISSN
- 1017-9909
- eISSN
- 1560-229X
- Language
- English
- Date published
- 1999
- 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
- 9984047757602771
Metrics
11 Record Views