Journal article
Automatic detection of red lesions in digital color fundus photographs
IEEE Transactions on Medical Imaging, Vol.24(5), pp.584-592
05/2005
DOI: 10.1109/TMI.2005.843738
PMID: 15889546
Abstract
The robust detection of red lesions in digital color fundus photographs is a critical step in the development of automated screening systems for diabetic retinopathy. In this paper, a novel red lesion detection method is presented based on a hybrid approach, combining prior works by Spencer et al. (1996) and Frame et al. (1998) with two important new contributions. The first contribution is a new red lesion candidate detection system based on pixel classification. Using this technique, vasculature and red lesions are separated from the background of the image. After removal of the connected vasculature the remaining objects are considered possible red lesions. Second, an extensive number of new features are added to those proposed by Spencer-Frame. The detected candidate objects are classified using all features and a k-nearest neighbor classifier. An extensive evaluation was performed on a test set composed of images representative of those normally found in a screening set. When determining whether an image contains red lesions the system achieves a sensitivity of 100% at a specificity of 87%. The method is compared with several different automatic systems and is shown to outperform them all. Performance is close to that of a human expert examining the images for the presence of red lesions.
Details
- Title: Subtitle
- Automatic detection of red lesions in digital color fundus photographs
- Creators
- Meindert Niemeijer - Image Sci. Inst., Utrecht, NetherlandsBram Van Ginneken - Image Sci. Inst., Utrecht, NetherlandsJoes Staal - Image Sci. Inst., Utrecht, NetherlandsMaria S A Suttorp-SchultenMichael D Abràmoff
- Resource Type
- Journal article
- Publication Details
- IEEE Transactions on Medical Imaging, Vol.24(5), pp.584-592
- DOI
- 10.1109/TMI.2005.843738
- PMID
- 15889546
- NLM abbreviation
- IEEE Trans Med Imaging
- ISSN
- 0278-0062
- eISSN
- 1558-254X
- Publisher
- IEEE
- Language
- English
- Date published
- 05/2005
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Ophthalmology and Visual Sciences
- Record Identifier
- 9983805905102771
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