Classification of retinal image for automatic cataract detection

• Main Authors: Meimei Yang, Ji-Jiang Yang, Qinyan Zhang, Yu Niu, Jianqiang Li
• Format: Conference Proceeding
• Language: English
• Subjects: Biomedical imaging; cataract; Educational institutions; Feature extraction; improved Topbottom hat transformation; neural network classifier; Noise; Optical filters; Optical imaging; Retina; retinal image processing; trilateral filter
• DOI: 10.1109/HealthCom.2013.6720761

Cataract is one of the most common diseases that might cause blindness. Previous research shows that cataract occupies almost 50% in severe visually impairments. Considering the fact that retinal image is one of the most important medical references that help to diagnose the cataract, this paper proposes to use a neural network classifier for automatic cataract detection based on the classification of retinal images. The classifier building procedure includes three parts: preprocessing, feature extraction, and classifier construction. In the pre-processing part, an improved Top-bottom hat transformation is proposed to enhance the contrast between the foreground and the object, and a trilateral filter is used to decrease the noise in the image. According to the analysis of pre-processed image, the luminance and texture message of the image are extracted as classification features. The classifier is constructed by back propagation (BP) neural network which has two layers. Based on the clearness degree of the retinal image, the patients' cataracts are classified into normal, mild, medium or severe ones. The initial evaluation results illustrate the effectiveness of our proposed approach, which has great potential to improve diagnosis efficiency of the ophthalmologist and reduce the physical and economic burden of the patients and society.