<title>Automated detection of changes in sequential color ocular fundus images</title>

Abstract

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.

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

“…The authors' objective is reducing the burden on the ophthalmologists performing ocular fundus inspection in health checks. They have been investigating methods in which the normality or abnormality of the ocular fundus image is automatically determined in order to reject normal cases [1,2]. At present, ocular fundus inspections for early detection of adult diseases such as hypertension and diabetes are always performed manually by ophthalmologists.…”