Identification of suitable fundus images using automated quality assessment methods.

Retinal image quality assessment (IQA) is a crucial process for automated retinal image analysis systems to obtain an accurate and successful diagnosis of retinal diseases. Consequently, the first step in a good retinal image analysis system is measuring the quality of the input image. We present an approach for finding medically suitable retinal images for retinal diagnosis.

Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images.

Diabetic retinopathy (DR) is one of the most important complications of diabetes mellitus, which causes serious damages in the retina, consequently visual loss and sometimes blindness if necessary medical treatment is not applied on time. One of the difficulties in this illness is that the patient with diabetes mellitus requires a continuous screening for early detection. So far, numerous methods have been proposed by researchers to automate the detection process of DR in retinal fundus images.

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.

Automatic segmentation of age-related macular degeneration in retinal fundus images.

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.