Automatic Brain and Tumor Segmentation
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
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
Automated detection of masses in mammograms by local adaptive thresholding
Computers in Biology and Medicine
An improved matched filter for blood vessel detection of digital retinal 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
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
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
Journal of Medical Systems
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Fovea center detection based on the retina anatomy and mathematical morphology
Computer Methods and Programs in Biomedicine
Data mining techniques for the screening of age-related macular degeneration
Knowledge-Based Systems
Computer Methods and Programs in Biomedicine
Computer-aided diagnosis of diabetic retinopathy: A review
Computers in Biology and Medicine
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Every year an increasing number of people are affected by age-related macular degeneration (ARMD). Consequently, vast amount of information is accumulated in medical databases and manual classification of this information is becoming more and more difficult. Therefore, there is an increasing interest in developing automated evaluation methods to follow up the diseases. In this paper, we have presented an automatic method for segmenting the ARMD in retinal fundus images. Previously used direct segmentation techniques, generating unsatisfactory results in some cases, are more complex and costly than our inverse method. This is because of the fact that the texture of unhealthy areas of macula is quite irregular and varies from eye to eye. Therefore, a simple inverse segmentation method is proposed to exploit the homogeneity of healthy areas of the macula rather than unhealthy areas. This method first extracts healthy areas of the macula by employing a simple region growing method. Then, blood vessels are also extracted and classified as healthy regions. In order to produce the final segmented image, the inverse image of the segmented image is generated as unhealthy region of the macula. The performance of the method is examined on various qualities of retinal fundus images. The segmentation method without any user involvement provides over 90% segmentation accuracy. Segmented images with reference invariants are also compared with consecutive images of the same patient to follow up the changes in the disease.