Title :
Automatic fundus image classification for computer-aided diagonsis
Author :
Lu, Shijian ; Liu, Jiang ; Lim, Joo Hwee ; Zhang, Zhuo ; Meng, Tan Ngan ; Wong, Wing Kee ; Li, Huiqi ; Wong, Tian Yin
Author_Institution :
Inst. for Infocomm Res., A*STAR, Singapore, Singapore
Abstract :
With the advances of computer technology, more and more computer-aided diagnosis (CAD) systems have been developed to provide the ldquosecond opinionrdquo. This paper reports an automatic fundus image classification technique that is designed to screen out the severely degraded fundus images that cannot be processed by traditional CAD systems. The proposed technique classifies fundus images based on the image range property. In particular, it first calculates a number of range images from a fundus image at different resolutions. A feature vector is then constructed based on the histogram of the calculated range images. Finally, fundus images can be classified by a linear discriminant classifier that is built by learning from a large number of normal and abnormal training fundus images. Experiments over 644 fundus images of different qualities show that the classification accuracy of the proposed technique reaches above 96%.
Keywords :
diseases; eye; image classification; medical image processing; vectors; CAD system; automatic fundus image classification; computer technology; computer-aided diagonsis; eye diseases; feature vector construction; fundus image vectorization; histogram; image properties; linear discriminant classifier; severely degraded fundus images; Diagnosis, Computer-Assisted; Fundus Oculi; Humans; Image Processing, Computer-Assisted; Reproducibility of Results; Retinal Diseases;
Conference_Titel :
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE
Conference_Location :
Minneapolis, MN
Print_ISBN :
978-1-4244-3296-7
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2009.5332917