KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Hamamoto, KazuhikoSubject: search.filters.subject.diabetic retinopathy

Search Results

Now showing 1 - 4 of 4
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Cotton wool spots detection in diabetic retinopathy based on adaptive thresholding and ant colony optimization coupling support vector machine
    (2019-06-01)
    Sreng, Syna
    ;
    Maneerat, Noppadol
    ;
    Hamamoto, Kazuhiko
    ;
    Panjaphongse, Ronakorn
    Diabetic retinopathy is the major issue of diabetes-induced blindness worldwide but is curable if detected in time. Cotton wool spots (CWSs) are the critical lesions of diabetic retinopathy, which indicate not only advanced nonproliferative but also preproliferative diabetic retinopathy. It is crucial to detect CWSs for grading the severity of diabetic retinopathy. By grading the severity of diabetic retinopathy accurately, the eye specialist can make an effective treatment plan to protect the patient's vision against blindness. CWSs detection remains challenging because of their uneven appearance, in which some CWSs are not clearly visible and some resemble hard exudates. This paper proposed an automatic CWS detection method based on adaptive thresholding and ant colony optimization (ACO) coupled with support vector machine (SVM). One-hundred and sixty-two features from five feature sets, namely morphologies, first-order statistics, gray-level co-occurrence matrix, gray-level run length matrix, and lacunarity, are extracted, and then four feature selection methods,namely genetic algorithm, particle swarm optimization, stepwise method, and ACO, are coupled with SVM classifiers. The evaluation results of the proposed methods on local, standard diabetic retinopathy database calibration level 1, and high-resolution fundus image database datasets containing 319 images indicate that ACO coupling cubic SVM performs better than the other pairs with sensitivity 90.16%, specificity 97.92%, accuracy 96.96%, and area under receiver operating characteristic curve 97.19%. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Primary screening of diabetic retinopathy based on integrating morphological operation and support vector machine
    (2017-07-02)
    Sreng, Syna
    ;
    Maneerat, Noppadol
    ;
    Isarakorn, Don
    ;
    Hamamoto, Kazuhiko
    ;
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Automated microaneurysms detection in fundus images using image segmentation
    (2017-04-19)
    Sreng, Syna
    ;
    Maneerat, Noppadol
    ;
    Hamamoto, Kazuhiko
    Diabetic retinopathy is one of the complicated diseases which occurs in diabetic patients when the affects damage the retina. The eyes vision can lead to be lost in case of late treatment. Microaneurysms are the earliest detectable abnormalities of diabetic retinopathy, so the automated detection of the lesions is essential and useful task. This paper proposed a simple method to detect microaneurysms based on its characteristics in fundus images using some techniques in image segmentation. First, we preprocessed to reduce image noise and improve the contrast. Then we segmented them using Canny edges detection and maximum entropy thresholding. The characteristics of microaneurysms which appear as small red dots and circular shape are the specific points to discriminate them from the other lesions as well as the anatomical structures of the fundus image by applying area and eccentricity methods. Finally, the morphological operation was applied to mark out these symptoms. 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 processing time is 9.53 seconds per image.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Automatic hemorrhages detection based on fundus images
    (2015-01-01)
    Sreng, Syna
    ;
    Maneerat, Noppadol
    ;
    Isarakorn, Don
    ;
    Hamamoto, Kazuhiko
    ;
    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.