Comparison of Colour Spaces for Optic Disc Localisation in Retinal Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
An improved matched filter for blood vessel detection of digital retinal images
Computers in Biology and Medicine
Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images
Journal of Medical Systems
Automatic segmentation of age-related macular degeneration in retinal fundus images
Computers in Biology and Medicine
Fully automatic segmentation of coronary vessel structures in poor quality x-ray angiogram images
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Locating human eyes using edge and intensity information
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
Computer vision algorithms for retinal image analysis: current results and future directions
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Data mining techniques for the screening of age-related macular degeneration
Knowledge-Based Systems
Computer Methods and Programs in Biomedicine
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Day by day, huge amount of information is collected in medical databases. These databases include quite interesting information that could be exploited in diagnosis of illnesses and medical treatment of patients. Classification of these data is getting harder as the databases are expanded. On the other hand, automated image analysis and processing is one of the most promising areas of computer vision used in medical diagnosis and treatment. In this context, retinal fundus images, offering very high resolutions that are sufficient for most of the clinical cases, provide many indications that could be exploited in diagnosing and screening retinal degenerations or diseases. Consequently, there is a strong demand in developing automated evaluation systems to utilize the information stored in the medical databases. This study proposes an automatic method for segmentation of ARMD in retinal fundus images. The method used in the automated system extracts lesions of the ARMD by employing a statistical method. In order to do this, the statistical segmentation method is first used to extract the healthy area of the macula that is more familiar and regular than the unhealthy parts. Here, characteristic images of the patterns of the macula are extracted and used to segment the healthy textures of an eye. In addition to this, blood vessels are also extracted and then classified as healthy regions. Finally, the inverse image of the segmented image is generated which determines the unhealthy regions of the macula. The performance of the method is examined on various quality retinal fundus images. Segmented images are also compared with consecutive images of the same patient to follow up the changes in the disease.