Abstract: Tessellated fundus is very common in myopic and aged eyes, and it is an important clinical marker for retinochoroidal changes. In order to assist the clinical diagnosis of the disease such as myopia, an approach for automatic tessellated fundus grading based on choroidal vessel extraction is proposed. Firstly, in order to process retinal images with different resolutions, image-size normalization based on the diameter of acquired region of interest(ROI) is performed in preprocessing. Next, optic disc center is located based on principle component analysis. A fundus is divided into 4 quadrants centered on optic disc center. Three types of features are extracted to describe the visibility of choroidal vessels, and the severity of tessellated fundus in every quadrant is classified into 0~3 grades using C4. 5 decision tree. Finally, the grading results of 4 quadrants are combined to classify a tessellated fundus into four classes:healthy, mild, moderate, and severe. The proposed approach was tested using 130 retinal images and the success rate of tessellated fundus grading can reach 84.7%. Our preliminary work illustrates the effectiveness of the proposed automatic tessellated fundus grading approach. Automatic grading of tessellated fundus can provide quantitative basis of ocular diseases related with tessellated fundus.