Image Mining Research Retinal Image Mining Project - RETINA Content-based Retrieval of Brain CT Scan Images Retinal Image Mining Project - RETINA Retinal images provide a window into what is happening inside the human body. Subtle changes in the eye's retinal vessels can serve as warnings as to whether the patient may be heading towards a stroke. Computers can help trace and track these vessels accurately and quantify the changes in them over time. The RETINA image mining project started several years ago which has resulted in a computer aided screening system for use in polyclinics. Recently, we have developed increasingly accurate and robust algorithms to grade the vascular or blood-vessel structure in retinal images automatically. Our approach incorporates techniques from wavelet analysis, texture analysis, and curvature ridge/trench analysis to attain the desired clinical sensitivity. In collaboration with the Department of Ophthalmology and the Singapore Eye Research Institute (SERI), we have developed a user-friendly system called SIVA (Singapore "I" Vessel Assessment) to extract vascular structure information and derive quantitative measures for the description of retinal vessels' characteristics. The robust system is also flexible and intuitive in gathering feedback for enhancing the accuracy of vessel measurement. Content-based Retrieval of Brain CT Scan Images Due to the advances of multi-slice Computed Tomography (CT) Scan with up to 64 slices per scan, a huge amount of CT images are produced in modern hospitals every year. However, the current hospital image database does not allow retrieval of images other than with patient names or identity card number. Very often doctors already overloaded with day-to-day medical consultation simply could not remember patients¡¯ names when they need to refer to similar cases seen before and as such valuable information are lost in the sea of raw image pixels. Interests in medical image retrieval have grown over the years. Current image retrieval techniques have largely been relying on the meta text data associated with the images. With the above background in mind, the present project aims to investigate techniques for fast retrieval of brain CT scan images based on the image content of the medical anomalies such as types of head trauma injuries shown below. Machine learning paradigms will be explored to enable automatic classification of medical images based on the image contents and textual data.