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Primary screening of diabetic retinopathy based on integrating morphological operation and support vector machine
(2017-07-02)
Sreng, Syna
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Maneerat, Noppadol
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Isarakorn, Don
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Hamamoto, Kazuhiko
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Panjaphongse, Ronakorn
Diabetic retinopathy is one of the most frequent causes of blindness due to diabetes. Primary screening is essential due to prerequisite step toward the diagnosis of diabetic retinopathy in order to prevent vision loss or blindness. This paper presents the methods to discriminate between healthy images and diabetic retinopathy images on the retinal images. The proposed method involves three main steps. Initially, the image is preprocessed to remove small noises and enhance the contrast of the image. Secondly, Kirsch edge detection is utilized to detect the bright lesions. Subsequently, the red lesions are detected depending on top-hat morphological filtering methods. Then the bright and dark lesions are combined by using logical AND operator. In order to be left only pathological signs, the noises near the vicinity of the optic disc and blood vessels are further removed using blob analysis. Finally, morphological features are extracted and fed to the SVM classifier. The proposed method was evaluated with three datasets containing 229 images. It achieved the accuracy of 90%, sensitivity of 86.33% and specificity of 98.55% with the average computational time 8 seconds per image. The method is simple and fast, easy to implement and the result is promising.
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Automatic hemorrhages detection based on fundus images
(2015-01-01)
Sreng, Syna
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Maneerat, Noppadol
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Isarakorn, Don
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Hamamoto, Kazuhiko
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Panjaphongse, Ronakorn
This paper proposes methods to detect hemorrhages which are known as a kind of lesions in diabetic retinopathy. To detect the symptom, eye fundus structures (blood vessels and fovea) as well as microaneuysms need to be discriminated to filter out only the hemorrhages. Five processing steps are proposed based analysis on fundus images. First, preprocessing step is processed to improve the quality of the image. Then all red features are filtered out. They include blood vessels, fovea, microaneurysms and hemorrhages. After that, morphology operation and compactness measurement are applied to eliminate the fovea, and blood vessels. Finally, hemorrhages can be classified by using area method to remove microaneurysms and some small noise. 579 fundus images from Bhumibol Adulyadej Hospital were tested. The results were analysis by ophthalmologist in order to define system accuracy and preciseness. According to results of comparison, we found that the accuracy is 90 % and the average of processing time is 6.23 seconds per image.
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Feature extraction from retinal fundus image for early detection of diabetic retinopathy
(2013-12-01)
Sreng, Syna
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Takada, Jun Ichi
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Maneerat, Noppadol
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Isarakorn, Don
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Varakulsiripunth, Ruttikorn
Automated detection of lesions in retinal fundus image can be aid in the detection of diabetic retinopathy. Exudates are the early sign of diabetic retinopathy so the proper detection of these lesions is an essential task in an automatic retinal screening. On the research work leading to automatic analysis of exudate detection, the knowledge of Optic Disk (OD) location is very useful. An efficient algorithm is presented to detect the OD and exudate which are the most important features for early detection of diabetic retinopathy. From a retinal fundus image, the proposed method first preprocesses and estimates the histogram of retinal background, then filters out the bright pixels in intensity image. They include OD, and non-OD (exudates and noise). Next, an OD boundary is determined and eliminated after applying blob boundary measurement and morphological reconstruction. Finally, exudates are extracted by applying the maximum entropy thresholding to filter out the bright pixels from the green component of retinal image which OD region inside is eliminated. The proposed technique has been tested first on 100 images from hospital. Experimental results show that 93% and 89% of OD and exudate were detected correctly, respectively. © 2013 IEEE.
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Automatic exudate extraction for early detection of Diabetic Retinopathy
(2013-01-01)
Sreng, Syna
;
Takada, Jun Ichi
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Maneerat, Noppadol
;
Isarakorn, Don
;
Pasaya, Bundit
Diabetic Retinopathy (DR) is the most common cause of blindness in diabetic patients, but early detection and timely treatment can prevent this problem. Exudates have been found to be one of the signs and serious DR anomalies so the proper detection of these lesions and the treatment should be done immediately to prevent loss of vision. The aim of this study is to automatically detect these lesions in fundus images. To achieve this goal, the proposed method first preprocesses to improve the quality of fundus image, and then Optic Disc (OD) is detected and eliminated to prevent the interference to the result of exudate detection by combination of 3 methods; image binarization, Region Of Interest (ROI) based segmentation and Morphological Reconstruction (MR). Next, exudates are detected by applying the maximum entropy thresholding to filter out the bright pixels from the result of OD region eliminated. Since the result contains some noises which appear as bright light at the edge of fundus area in some images, that affect is considered and eliminated to improve the result of false positive. Finally, exudates are extracted by using MR. The proposed technique has been tested on 100 fundus images from hospital. Experimental results show that 91 % of exudate is extracted correctly with the average process of 3.92 second per image. © 2013 IEEE.

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