Conference proceeding
Segmentation of the optic nerve head combining pixel classification and graph search
Proceedings of SPIE, Vol.6512(1), pp.651215-6512110
Medical Imaging 2007: Image Processing
03/08/2007
DOI: 10.1117/12.710588
Abstract
Early detection of glaucoma is essential to minimizing the risk of visual loss. It has been shown that a good predictor of glaucoma is the cup-to-disc ratio of the optic nerve head. This paper presents an automated method to segment the optic disc. Our approach utilizes pixel feature selection to train a feature set to recognize the region of the disc. Soft pixel classification is used to generate a probability map of the disc. A new cost function is developed for maximizing the probability of the region within the disc. The segmentation of the image is done using a novel graph search algorithm capable of detecting the border maximizing the probability of the disc. The combination of graph search and pixel classification enables us to incorporate large feature sets into the cost function design, which is critical for segmentation of the optic disc. Our results are validated against a reference standard of 82 datasets and compared to the manual segmentations of 3 glaucoma fellows.
Details
- Title: Subtitle
- Segmentation of the optic nerve head combining pixel classification and graph search
- Creators
- Michael B Merickel Jr - The Univ. of IowaMichael D Abràmoff - The Univ. of Iowa and VA Medical CtrMilan Sonka - The Univ. of IowaXiaodong Wu - The Univ. of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of SPIE, Vol.6512(1), pp.651215-6512110
- Conference
- Medical Imaging 2007: Image Processing
- DOI
- 10.1117/12.710588
- ISSN
- 0277-786X
- Language
- English
- Date published
- 03/08/2007
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984047750302771
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