A Novel Grading Method of Cataract Based on AWM

Cataract is one of the most common causes of visual blindness, about 90% of the elderly over 60 years old with visual impairment in China have cataract diseases, and about 90% of eye diseases are diagnosed by observing the fundus. The observation of fundus has always been a necessary mean of diagnosing a cataract. Moreover, it is highly uncertain about judging the degree of lesions based on experience, but also the efficiency of this method is very low. Therefore, employing a computer-aided diagnostic system to perform the automatic grading of cataract is of great research value of practical use. Most of the studies reported in the literature utilize histogram equalization (Histeq) or other image enhancement methods based on gray value changes to improve the contrast. In this paper, the adaptive window model (AWM) is used to enhance the contrast between the vessel and the background. We used features extracted from the spoke features of the image for cataract grading. The best average accuracy achieved by Support Vector Machine (Back Propagation Neural Network) is 80.12% (78.26%) when AWM is used to enhance the contrast. Furthermore, it is even higher than 73.29% (75.16%) when the Histeq is used as an image enhancement technique.