Automated Retinal Vessel Segmentation based on Morphology and Random Forest classifier
Journal Title: International Journal of Engineering and Science Invention - Year 2018, Vol 7, Issue 3
Abstract
An accurate extraction of the retinal vessel is one of the most important tasks in the field of medical image processing to diagnose various retinal as well as non-retinal diseases. This paper presents an automated method for the segmentation of retinal vasculature using morphological processing and the Random forest classifier. The proposed method starts with the extraction of the thin vessels using morphological processing. The Random Forest classifier utilizing 21-D feature vector is used to extract the major vessels. The final segmented image is obtained by combining both the major and the thin vessels. The feature vector for each pixel is constructed by using color component features (3-D), morphological features (10-D) and the Gabor filter responses (8-D).The performance of this method was evaluated and tested using the retinal images in the available retinal databases, namely, DRIVE, STARE and CHASE-DB1.
Authors and Affiliations
T. Sumathi, Dr. P. Vivekanandan, Dr. Ravikanth Balaji
Keywords
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