Robust Object Extraction and Change Detection in Retinal Images for Diabetic Clinical Studies (original) (raw)

2007, … Intelligence in Image and …

With the rapid advances in computing and electronic imaging technology, there has been increasing interest in developing computer aided medical diagnosis systems to improve the medical service for the public. Images of ocular fundus provide crucial observable features for diagnosing many kinds of pathologies such as diabetes, hypertension, and arteriosclerosis. A computer-aided retinal image analysis system can help eye specialists to screen larger populations and produce better evaluation of treatment and more effective clinical study. This paper is focused on the immediate needs for clinical studies on diabetic patients. Our system includes multiple feature extraction, robust retinal vessel segmentation, hierarchical change detection and classification. The output throughout this system will assist doctors to speed up screening large populations for abnormal cases, and facilitate evaluation of treatment for clinical study. I. INTRODUCTION ITH the fast advances in computing technology and computer industry, multimedia data such as digital signal, image, document, audio, graphics, and video have become widely used in different areas. The aim of the development of automatic medical diagnosis systems for medical applications is to provide storage, processing, and communication services required by the medical community effectively and reliably. Reliable and accurate medical diagnosis requires knowledge of changes in different clinical symptoms due to health degeneration and disease deterioration. One of the main critical issues of such systems is the handling of multimedia medical information in a uniform way to analyze medical data accurately and diagnose different diseases reliably. Image processing techniques offer the means to acquire digital information, at different scales, quickly and efficiently. This paper is focused on the immediate needs for clinical studies on diabetic patients. To tackle key issues in image understanding, we propose to investigate, design, analyze, implement and evaluate new algorithms for feature extraction, segmentation, region representation and classification. The proposed system includes extracting multiple image features via wavelet transforms; segmenting