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. We used a three-class grading system that consists of good, bad, and outlier classes. We created a retinal image quality dataset with a total of 216 consecutive images called the Diabetic Retinopathy Image Database. We identified the suitable images within the good images for automatic retinal image analysis systems using a novel method. Subsequently, we evaluated our retinal image suitability approach using the Digital Retinal Images for Vessel Extraction and Standard Diabetic Retinopathy Database Calibration level 1 public datasets. The results were measured through the F1 metric, which is a harmonic mean of precision and recall metrics. The highest F1 scores of the IQA tests were 99.60%, 96.50%, and 85.00% for good, bad, and outlier classes, respectively. Additionally, the accuracy of our suitable image detection approach was 98.08%. Our approach can be integrated into any automatic retinal analysis system with sufficient performance scores.

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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.

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