Improvement of automatic hemorrhages detection methods using brightness correction on fundus images - art. no. 69153E
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.
Full-textDOI: · Available from: Hiroshi Fujita, May 28, 2015
SourceAvailable from: Yuji Hatanaka[Show abstract] [Hide abstract]
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.IEEE transactions on medical imaging 01/2010; 29(1):185-195. DOI:10.1109/TMI.2009.2033909. · 3.54 Impact Factor
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ABSTRACT: Microaneurysm in the retina is one of the signs of simple diabetic retinopathy. We have been investigating a computerized method for the detection of microaneurysms on retinal fundus images. In this study, the computerized scheme was developed by using twenty five cases. After image preprocessing, candidate regions for microaneurysms were detected using a double-ring filter. Any potential false positives located in the regions corresponding to blood vessels were removed by automatic extraction of blood vessels from the images. One hundred twenty six image features were determined, and 28 components were selected by using principal component analysis, and the candidate lesions were classified into microaneurysms or false positives using the rule-based method and an artificial neural network. The true positive rate of the proposed method was 68% at 15 false positives per image.
Conference Paper: A survey on hemorrhage detection in diabetic retinopathy retinal images[Show abstract] [Hide abstract]
ABSTRACT: Diabetic Retinopathy is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. The presence of hemorrhages in the retina is the earliest symptom of diabetic retinopathy. The number and shape of hemorrhages is used to indicate the severity of the disease. Early automated hemorrhage detection can help reduce the incidence of blindness. In this paper we review techniques, algorithms, and methodologies used for the detection of hemorrhage from diabetic retinopathy retinal images.Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2012 9th International Conference on; 01/2012