A statistical segmentation method for measuring age-related macular degeneration in retinal fundus images.

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.