Image registration, color correction, and change detection based on value of difference in sequential ocular fundus images

Abstract

SUMMARYThe purpose of this study is to assist ophthalmologic screening in health checks. A decision process for detecting progressive change is proposed in which normal cases are rejected on the basis of comparison between the ocular fundus image to be examined and the normal ocular fundus image of the same subject. Ocular fundus images contain large individual differences, but there is no progressive change in the normal condition. A large change in the image is observed only when there is a pathological change, such as white spots or hemorrhage. Consequently, an automatic screening procedure is considered in which an ocular fundus image diagnosed as normal is prepared for each individual, and images are judged to be normal when no progressive change is indicated by the difference between images. Specifically, an algorithm which consists of the following procedures is created. Image registration is applied, considering the ocular fundus shape of the individual.Color correction is applied to correct the brightness and color difference due to imaging conditions. The images are compared and the difference between the images is calculated and the presence or absence of progressive changes is decided. The algorithm was evaluated by using real ocular fundus images, and a rejection rate of 78% for normal cases was obtained. Thus, it is possible to assist screening by providing a pathological index

Automated analysis of the distributions and geometries of blood vessels on retinal fundus images

“…Their algorithm tracked vessels using a set of two-dimensional correlation kernels that act as low-pass differentiators perpendicular to the direction in which the vessels run and to the low-pass averaging filters along the vessels. Sakuma et al presented a method for correcting colors and detecting any changes based on the values for differences in sequential ocular retinal images [5]. The method we propose detects vessel furcations [6].…”

“…Recently, many studies on the CAD system for fundus images have been reported [1][2][3][4][5][6][7]. Tolias et al presented a method for detecting blood vessels [1].…”

“…The frequent use of this technique, however, may impose a heavy burden on ophthalmologists. Therefore, many research groups have made efforts in developing their own computer-aided diagnosis (CAD) systems based on retinal fundus images [1][2][3][4][5][6][7].…”