Automatic Microaneurysm Detection and Characterization Through Digital Color Fundus Images
Ocular fundus images can provide information about retinal, ophthalmic, and even systemic diseases such as diabetes. Microaneurysms (MAs) are the earliest sign of Diabetic Retinopathy, a frequently observed complication in both type 1 and type 2 diabetes. Robust detection of MAs in digital color fundus images is critical in the development of automated screening systems for this kind of disease. Automatic grading of these images is being considered by health boards so that the human grading task is reduced. In this paper we describe segmentation and the feature extraction methods for candidate MAs detection.We show that the candidate MAs detected with the methodology have been successfully classified by a MLP neural network (correct classification of 84percent).
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- Computational Research Division
- DOE Contract Number:
- DE-AC02-05CH11231
- OSTI ID:
- 979799
- Report Number(s):
- LBNL-2856E; TRN: US201011%%405
- Resource Relation:
- Conference: International Joint Conference - Brazilian Symposium on Artificial Intelligence and Brazilian Symposium on Neural Networks - II Workshop on Computational Intelligence, Salvador, Bahia- Brazil
- Country of Publication:
- United States
- Language:
- English
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