Improvement of automatic hemorrhages detection methods using brightness correction on fundus images

Dept. of Electronic Control Engineering, Gifu National College of Technology, 2236-2, 501-0495, Kamimakuwa, Gifu, Japan; Dept. of Intelligent Image Information, Division of Regeneration and Advanced Med. Science Graduate School of Medicine, Gifu University, 501-1194, Gifu, Japan; Tak Co., Ltd, 4-32-12, 503-0803, Kono, Gifu, Japan; Dept. of Ophthalmology, School of Medicine, Gifu University, 501-1194, Gifu, Japan
Proc SPIE 04/2008; 81:58-320. DOI: 10.1117/12.771051

ABSTRACT We have been developing several automated methods for detecting abnormalities in fundus images. The purpose of this study is to improve our automated hemorrhage detection method to help diagnose diabetic retinopathy. We propose a new method for preprocessing and false positive elimination in the present study. The brightness of the fundus image was changed by the nonlinear curve with brightness values of the hue saturation value (HSV) space. In order to emphasize brown regions, gamma correction was performed on each red, green, and blue-bit image. Subsequently, the histograms of each red, blue, and blue-bit image were extended. After that, the hemorrhage candidates were detected. The brown regions indicated hemorrhages and blood vessels and their candidates were detected using density analysis. We removed the large candidates such as blood vessels. Finally, false positives were removed by using a 45-feature analysis. To evaluate the new method for the detection of hemorrhages, we examined 125 fundus images, including 35 images with hemorrhages and 90 normal images. The sensitivity and specificity for the detection of abnormal cases was were 80% and 88%, respectively. These results indicate that the new method may effectively improve the performance of our computer-aided diagnosis system for hemorrhages.

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    Dataset: 4- GVIP
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    ABSTRACT: Abnormalities of retinal vasculatures can indicate health conditions in the body, such as the high blood pressure and diabetes. Providing automatically determined width ratio of arteries and veins (A/V ratio) on retinal fundus images may help physicians in the diagnosis of hypertensive retinopathy, which may cause blindness. The purpose of this study was to detect major retinal vessels and classify them into arteries and veins for the determination of A/V ratio. Images used in this study were obtained from DRIVE database, which consists of 20 cases each for training and testing vessel detection algorithms. Starting with the reference standard of vasculature segmentation provided in the database, major arteries and veins each in the upper and lower temporal regions were manually selected for establishing the gold standard. We applied the black top-hat transformation and double-ring filter to detect retinal blood vessels. From the extracted vessels, large vessels extending from the optic disc to temporal regions were selected as target vessels for calculation of A/V ratio. Image features were extracted from the vessel segments from quarter-disc to one disc diameter from the edge of optic discs. The target segments in the training cases were classified into arteries and veins by using the linear discriminant analysis, and the selected parameters were applied to those in the test cases. Out of 40 pairs, 30 pairs (75%) of arteries and veins in the 20 test cases were correctly classified. The result can be used for the automated calculation of A/V ratio.
    Proc SPIE 03/2010;
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    ABSTRACT: The detection of microaneurysms in digital color fundus photographs is a critical first step in automated screening for diabetic retinopathy (DR), a common complication of diabetes. To accomplish this detection numerous methods have been pub-lished in the past but none of these was compared with each other on the same data. In this work we present the results of the first international microaneurysm detection competition, organized in Manuscript received August 04,. B. van Ginneken is with the Image Sciences Institute, 3584 CX Utrecht, The the context of the Retinopathy Online Challenge (ROC), a multi-year online competition for various aspects of DR detection. For this competition, we compare the results of five different methods, produced by five different teams of researchers on the same set of data. The evaluation was performed in a uniform manner using an algorithm presented in this work. The set of data used for the competition consisted of 50 training images with available refer-ence standard and 50 test images where the reference standard was witheld by the organizers (M. Niemeijer, B. van Ginneken, and M. D. Abràmoff). The results obtained on the test data was submitted through a website after which standardized evaluation software was used to determine the performance of each of the methods. A human expert detected microaneurysms in the test set to allow comparison with the performance of the automatic methods. The overall results show that microaneurysm detection is a challenging task for both the automatic methods as well as the human expert. There is room for improvement as the best performing system does not reach the performance of the human expert. The data associated with the ROC microaneurysm detec-tion competition will remain publicly available and the website will continue accepting submissions.
    01/2010; 29.

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