Role of Image Contrast Enhancement Technique for Ophthalmologist as Diagnostic Tool for Diabetic Retinopathy

Soomro, Toufique Ahmed; Gao, Junbin; Khan, Muhammad Mushtaq; Khan, Tariq M.; Paul, Manoranjan

doi:10.1109/dicta.2016.7797078

Cited by 29 publications

(11 citation statements)

References 16 publications

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“…Among the eye complications, serious eye diseases such as DR are the primary leading disease as they cause blindness, especially in the working-age population [3,4]. Based on the estimate, according to the World Health Organization, the number of people with diabetes increased from 108 million in 1980 to 422 million in 2014 [5,6]. Prevalence is rising faster in low-and middle-income countries than in high-income countries.…”

Section: Introductionmentioning

confidence: 99%

“…Early treatment may be feasible by diagnosing the changes and tracking their progression, and these measures give an inexpensive treatment alternative. As a result, reconstructing a distinct network of vessels from retinal images aids in either quantifying the severity of the disease or evaluating the impact of routine eye treatment [6]. The analysis takes place using an image called a retinal image [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation

Soomro

Ali

Jandan³

et al. 2021

Electronics

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Segmentation of retinal vessels plays a crucial role in detecting many eye diseases, and its reliable computerized implementation is becoming essential for automated retinal disease screening systems. A large number of retinal vessel segmentation algorithms are available, but these methods improve accuracy levels. Their sensitivity remains low due to the lack of proper segmentation of low contrast vessels, and this low contrast requires more attention in this segmentation process. In this paper, we have proposed new preprocessing steps for the precise extraction of retinal blood vessels. These proposed preprocessing steps are also tested on other existing algorithms to observe their impact. There are two steps to our suggested module for segmenting retinal blood vessels. The first step involves implementing and validating the preprocessing module. The second step applies these preprocessing stages to our proposed binarization steps to extract retinal blood vessels. The proposed preprocessing phase uses the traditional image-processing method to provide a much-improved segmented vessel image. Our binarization steps contained the image coherence technique for the retinal blood vessels. The proposed method gives good performance on a database accessible to the public named DRIVE and STARE. The novelty of this proposed method is that it is an unsupervised method and offers an accuracy of around 96% and sensitivity of 81% while outperforming existing approaches. Due to new tactics at each step of the proposed process, this blood vessel segmentation application is suitable for computer analysis of retinal images, such as automated screening for the early diagnosis of eye disease.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation

Soomro

Ali

Jandan³

et al. 2021

Electronics

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“…Moreover, these techniques can not fully comprehend and eradicate the problem of low illumination and poor contrast regions in retinal fundus images. Although, contrast enhancement techniques are used as a pre-processing step that partially address the issue but they intensify the noise or artifacts present in the image [11,23] which led to the use of noise removal as an additional pre-processing step in some recent unsupervised methods [24,25].…”

Section: Introductionmentioning

confidence: 99%

DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images

Raza

Naveed

Akram³

et al. 2021

PLoS ONE

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In this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.

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“…Then after performing the task of ML methods and FE are utilized to categorize the diabetic retinopathy of the various phases as severe, mild and moderate. In [4], the author introduced the method to enhance the quality of image that contain the operation of morphological on the fundus image with the CLAHE to improve vessels in image. In [5], author demonstrated novel techniques of the imaging transformation for enhancement of the retinal images such as counterlet transform, wavelet transform and curvelet transform.…”

Section: Introductionmentioning

confidence: 99%

Identification of Diabetic Retinopathy from fundus images using CNNs

Math*¹,

Fatima²

2019

IJITEE

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the Diabetic Retinopathy is the diabetes-mellitus to human vision that is the main cause of vision loss. The early stage detection of diabetic retinopathy is can play eminent role in the diabetes treatment. The fundus of retinal image is utilized to recognize the symptoms of diabetic retinopathy. Moreover, the above phenomena led us to propose this paper; here we propose segment based learning approach for identification of diabetic retinopathy. The segment based image level is required to obtain the identification of diabetic retinopathy images, the classifiers and features are equally learned from the data. Then, we adapt pre-trained CNN as the fine tune to achieve the segment level estimation of diabetic retinopathy. For identification of diabetic retinopathy, we achieve accuracy 96.97 and 98.46% at 20 and 30% and also achieve AUC (Area under Curve) 97.51 and 98.50 at 20 and 30% on the Kaggle dataset. Our proposed model outperforms much better than other models.

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Role of Image Contrast Enhancement Technique for Ophthalmologist as Diagnostic Tool for Diabetic Retinopathy

Cited by 29 publications

References 16 publications

Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation

Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation

DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images

Identification of Diabetic Retinopathy from fundus images using CNNs

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