Retinal haemorrhages segmentation using improved toboggan segmentation algorithm in diabetic retinopathy images

Karkuzhali, S.; Manimegalai, D.

doi:10.4066/biomedicalresearch.29-17-820

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…In order to retrieve the OD and OC regions, Karkuzhali et al [18] used super pixel segmentation to calculate CDR, ISNT, optic nerve head, and the area of the blood vessels are measured. The neural networks with 100% accuracy were trained using 26 images.…”

Section: Methods For Classificationmentioning

confidence: 99%

Hybrid glaucoma detection model based on reflection components separation from retinal fundus images

Mayaluri

Lenka

2023

EAI Endorsed Trans Perv Health Tech

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The diagnosis of diseases associated to the retina is significantly aided by retinal fundus images. However, when flash illumination is used during image acquisition, specularity reflection can occur on images. The retinal image processing applications are popular now days in diseases detection such as glaucoma, diabetic retinopathy, and cataract. Many modern disease detection algorithms suffer from performance accuracy limitation due to the creation of specularity reflection problem. This research proposes a hybrid model for screening of glaucoma which includes a preprocessing step to separate specular reflections from corrupted fundus images, a segmentation step using modified U-Net CNN, a feature extraction step, and an image classification step using support vector machine (SVM) with different kernels. Firstly, the diffuse and specular components are obtained using seven existing methods and apply a filter having high emphasis with a function called similar in each component. The best method, which provides highest quality images, is chosen among the seven compared methods and the output image is used in next steps for screening of glaucoma. The experimental results of the proposed model show that in preprocessing step, maximum improvement in terms of PSNR and SSIM are 37.97 dB and 0.961 respectively. For glaucoma detection experiment the results have the accuracy, sensitivity, and specificity of 91.83%, 96.39%, and 95.37% respectively and AUROC of 0.971.

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Section: Methods For Classificationmentioning

confidence: 99%

Hybrid glaucoma detection model based on reflection components separation from retinal fundus images

Mayaluri

Lenka

2023

EAI Endorsed Trans Perv Health Tech

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“…For hemorrhages detection, the model showed an AUC of 0.614. Karkuzhali and Manimegalai [32] includes two preprocessing steps. The median filter is the initial preprocessing step, followed by the Sobel operator.…”

Section: Related Previous Workmentioning

confidence: 99%

Hemorrhage semantic segmentation in fundus images for the diagnosis of diabetic retinopathy by using a convolutional neural network

et al. 2022

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Because retinal hemorrhage is one of the earliest symptoms of diabetic retinopathy, its accurate identification is essential for early diagnosis. One of the major obstacles ophthalmologists face in making a quick and effective diagnosis is viewing too many images to manually identify lesions of different shapes and sizes. To this end, researchers are working to develop an automated method for screening for diabetic retinopathy. This paper presents a modified CNN UNet architecture for identifying retinal hemorrhages in fundus images. Using the graphics processing unit (GPU) and the IDRiD dataset, the proposed UNet was trained to segment and detect potential areas that may harbor retinal hemorrhages. The experiment was also tested using the IDRiD and DIARETDB1 datasets, both freely available on the Internet. We applied preprocessing to improve the image quality and increase the data, which play an important role in defining the complex features involved in the segmentation task. A significant improvement was then observed in the learning neural network that was able to effectively segment the bleeding and achieve sensitivity, specificity and accuracy of 80.49%, 99.68%, and 98.68%, respectively. The experimental results also yielded an IoU of 76.61% and a Dice value of 86.51%, showing that the predictions obtained by the network are effective and can significantly reduce the efforts of ophthalmologists. The results revealed a significant increase in the diagnostic performance of one of the most important retinal disorders caused by diabetes.

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“…The region growing-based technique for exudates segmentation has achieved 97.9% sensitivity and 99.4% specificity. Karkuzhali et al 8 presented another region growing-based technique for hemorrhage segmentation. The researchers performed two pre-processing methods: (1) Median filter used to remove noise and irregular illumination.…”

Section: Retinal Lesion Segmentationmentioning

confidence: 99%

UNIConv: An enhanced U‐Net based InceptionV3 convolutional model for DR semantic segmentation in retinal fundus images

Kaur

2022

Concurrency and Computation

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Segmentation of diabetic retinopathy is a significant requirement for correct severity estimation level. Due to dissimilarity in shapes, size, and quantity of retinal lesions, the procedure of labor-intensive type of grading becomes a more time-consuming and challenging task. It imposes the prerequisite for an automated segmentation framework that can accurately identify the regions with margins and aid ophthalmologists in the early detection and monitoring of the severity level. The proposed work introduces a customized U-Net model based on the InceptionV3 pre-trained CNN network. It uses a periodic shuffling approach along with sub-pixel convolution initialized to convolution nearest-neighbor resize. The model performed training and validation for non-proliferative diabetic retinopathy that is, microaneurysms, hemorrhages, hard and soft exudates on two standard datasets such as IDRiD and DIARETDB1. The proposed model achieves 99.82% accuracy, 88.39% sensitivity, and 99.94% specificity for hemorrhages, 99.95% accuracy, 70.76% sensitivity, and 99.98% specificity for microaneurysms, 99.83% accuracy, 85.9% sensitivity, and 99.95% specificity for hard exudates, along with 99.62% accuracy, 85.38% sensitivity, and 99.71% specificity for soft exudates segmentation on the IDRID dataset. In comparison with both datasets, the proposed system on the IDRID dataset performed accurately with superior state-of-the-art outcomes for segmentation of the retinal lesions.

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Retinal haemorrhages segmentation using improved toboggan segmentation algorithm in diabetic retinopathy images

Cited by 5 publications

References 8 publications

Hybrid glaucoma detection model based on reflection components separation from retinal fundus images

Hybrid glaucoma detection model based on reflection components separation from retinal fundus images

Hemorrhage semantic segmentation in fundus images for the diagnosis of diabetic retinopathy by using a convolutional neural network

UNIConv: An enhanced U‐Net based InceptionV3 convolutional model for DR semantic segmentation in retinal fundus images

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