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
A recent trend is the automatic screening of color ocular fundus images. The examination of such images is used in the early detection of several adult diseases such as hypertension and diabetes. Since this type of examination is easier than CT, costs less, and has no harmful side effects, it will become a routine medical examination. Normal ocular fundus images are found in more than 90% of all people. To deal with the increasing number of such images, this paper proposes a new approach to process them automatically and accurately. Our approach, based on individual comparison, identifies changes in sequential images: a previously diagnosed normal reference image is compared to a non-diagnosed image.This paper presents a new approach for measuring changes in sequential color ocular fundus images and presents test results that prove its detection efficiency. Two processes that are required are registration and the detection of color differences in the two images. First, the images are registered based on the individual's ocular fundus structure. Second, the images are divided into blocks and for each pair of blocks, the chromaticity and luminosity are extracted. This block-wise division offsets the effect of environmental variations which extend over much larger areas than the blocks. If one block diverges sufficiently from its reference, it signifies a change in the block.The initial screening results, 350 image pairs, showed that 86% of the pairs (50 abnormal and 300 normal image pairs) had no significant difference and were successfully detected as normal with no false negatives. This approach suits the processing of image pairs where there is a need to detect small changes accurately regardless of environmental changes. Prior to clinical use, the proposed approach must be more extensively evaluated using a wide sample.
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