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Cataract remains a leading cause for blindness worldwide. Cataract diagnosis via human grading is subjective and time-consuming. Several methods of automatic grading are currently available, but each of them suffers from some drawbacks. In this paper, a new approach for automatic detection based on texture and intensity analysis is proposed to address the problems of existing methods and improve the performance from three aspects, namely ROI detection, lens mask generation and opacity detection. In the detection method, image clipping and texture analysis are applied to overcome the over-detection problem for clear lens images and global thresholding is exploited to solve the under-detection problem for severe cataract images. The proposed method is tested on 725 retro-illumination lens images randomly selected from a database of a community study. Experiments show improved performance compared with the state-of-the-art method.

Cataract is the leading cause of blindness worldwide. In this paper, registration of retroillumination image is investigated for detection of cortical and posterior subcapsular cataract (PSC). The pupil region is detected by local entropy filtering. The anterior image and posterior image are registered using maximum normalized cross-correlation. The registration of anterior and posterior image can be used to detect opacity in the future. The proposed algorithms were tested using clinical data and the experimental results indicate that the proposed method could facilitate automatic cataract diagnosis.