Measurement and Classification Retinal Blood Vessel Tortuosity in Digital Fundus Images

Analysis and detection of retinal blood vessels structure changes is the most important for diagnosing and detecting retinal diseases. Retinal blood vessels are normally straight or curved gently, but they tend to dilate and expand into twisting with age or the number of retinal disease. Tortuosity is a qualitative parameter used by ophthalmologist to show how blood vessels tortuos such as mild, moderate, severe and extreme as the analysis result remain subjective. To establish the relationship between tortuosity and vascular pathology requires quantitative measurement of tortuosity. This research developed a computer aided diagnosis (CAD) to detect retinal blood vessels before measure the blood vessels tortuosity and classify them. A method of morphological reconstruction is proposed to detect retinal blood vessels. Retinal blood vessel tortuosity was calculated using relative length variation method to classify retinal images. This research is conducted two types classification using K nearest neighbor. The first classification is normal and tortuosity classes. The second classification is moderate and severe tortuosity classes. The evaluation result for retinal blood vessel detection is obtained accuracy of 96.2%. Calculation of retinal blood vessel tortuosity based on relative length variation method because it has the best correlation of 0.892 to grading. The results of retinal blood vessel tortuosity classification between normal and tortuosity classes is obtained the best accuracy of KNN by 93%. The classification between moderate and severe tortuosity classes using KNN obtains accuracy of 100%. The proposed method can assist the ophthalmologist to detect blood vessels and calculate tortuosity of blood vessels to diagnose retinal diseases.