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.