This paper presents a fundus image analysis based computer aided system for automatic classification and grading of cataract, which provides great potentials to reduce the burden of well-experienced ophthalmologists (the scarce resources) and help cataract patients in under-developed areas to know timely their cataract conditions and obtain treatment suggestions from doctors. The system is composed of fundus image pre-processing, image feature extraction, and automatic cataract classification and grading. The wavelet transform and the sketch based methods are investigated to extract from fundus image the features suitable for cataract classification and grading. After feature extraction, a multiclass discriminant analysis algorithm is used for cataract classification, including two-class (cataract or non-cataract) classification and cataract grading in mild, moderate, and severe. A real-world dataset, including fundus image samples with mild, moderate, and severe cataract, is used for training and testing. The preliminary results show that, for the wavelet transform based method, the correct classification rates of two-class classification and cataract grading are 90.9% and 77.1%, respectively. The correct classification rates of two-class classification and cataract grading are 86.1% and 74.0% for the sketch based method, which is comparable to the wavelet transform based method. The pilot study demonstrates that our research on fundus image analysis for cataract classification and grading is very helpful for improving the efficiency of fundus image review and ophthalmic healthcare quality. We believe that this work can serve as an important reference for the development of similar health information system to solve other medical diagnosis problems. (C) 2014 Elsevier B.V. All rights reserved.