1. Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach
- Author
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Cleyson M. Kitamura, Daniel Welfer, Melissa Manfroi Dal Pizzol, Laura W. B. Ludwig, Diane Ruschel Marinho, and Jacob Scharcanski
- Subjects
Databases, Factual ,genetic structures ,Fundus Oculi ,Computer science ,Optic Disk ,Optic disk ,Health Informatics ,Fundus (eye) ,Mathematical morphology ,chemistry.chemical_compound ,Retinal Diseases ,Software Design ,Image Interpretation, Computer-Assisted ,Humans ,Image acquisition ,Computer vision ,Segmentation ,Diabetic Retinopathy ,business.industry ,Retinal ,eye diseases ,Computer Science Applications ,chemistry ,Computer-aided diagnosis ,Microvessels ,Automatic segmentation ,sense organs ,Artificial intelligence ,business ,Algorithms - Abstract
The identification of some important retinal anatomical regions is a prerequisite for the computer aided diagnosis of several retinal diseases. In this paper, we propose a new adaptive method for the automatic segmentation of the optic disk in digital color fundus images, using mathematical morphology. The proposed method has been designed to be robust under varying illumination and image acquisition conditions, common in eye fundus imaging. Our experimental results based on two publicly available eye fundus image databases are encouraging, and indicate that our approach potentially can achieve a better performance than other known methods proposed in the literature. Using the DRIVE database (which consists of 40 retinal images), our method achieves a success rate of 100% in the correct location of the optic disk, with 41.47% of mean overlap. In the DIARETDB1 database (which consists of 89 retinal images), the optic disk is correctly located in 97.75% of the images, with a mean overlap of 43.65%.
- Published
- 2010
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