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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    ABSTRACT: Diabetes is a chronic end organ disease that occurs when the pancreas does not secrete enough insulin or the body is unable to process it properly. Over time, diabetes affects the circulatory system, including that of the retina. Diabetic retinopathy is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. Ophthalmologists recognize diabetic retinopathy based on features, such as blood vessel area, exudes, hemorrhages, microaneurysms and texture. In this paper we review algorithms used for the extraction of these features from digital fundus images. Furthermore, we discuss systems that use these features to classify individual fundus images. The classifications efficiency of different DR systems is discussed. Most of the reported systems are highly optimized with respect to the analyzed fundus images, therefore a generalization of individual results is difficult. However, this review shows that the classification results improved has improved recently, and it is getting closer to the classification capabilities of human ophthalmologists.
    Journal of Medical Systems 02/2012; 36(1):145-57. · 1.78 Impact Factor
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    ABSTRACT: Retinal disorders are among the most dangerous diseases because many of them may lead to blindness if not early diagnosed and managed. Medical screening is very important as it allows the early diagnosis of the disease. Only ophthalmologists who have enough experience can differentiate between normal and abnormal retinas at the early stages of the disease. So, screening routines of the retina are very expensive and rarely to be done. In this work we provide a computer-aided screening system for the retinal disorders. The system could be used easily by the young physicians as it can automatically detect the early symptoms of abnormalities such as microaneurysms, hemorrhage and exudates. Using newly proposed image processing algorithms, we implemented a screening method based on the experience of the ophthalmology experts
    International Journal of graphics, vision and image processing (GVIP). 04/2012; 12(1).

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