Abnormality Detection in Eye Fundus Retina

Ashame, Laurine A.; Youssef, Sherin M.; Fayed, Salema F.

doi:10.1109/comapp.2018.8460270

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…So, the method is scale and rotation invariant. It can be seen that the employed OD detection algorithm achieves better values of sensitivity compared with the results presented by Walter and Klein (2001) for local hospital images, (Aquino et al 2010) for MESSIDOR database, (Allam et al 2016, Ashame et al 2018, and for DiaretDB1. The results obtained by ter Haar (2005) for local dataset, Zhang and Zhao (2014) for DiaretDB1, Allam et al (2016), and for DRIONS-DB dataset are better as compared with our method in terms of sensitivity, but the results are highly dependent on blood vessel tree, and finding the accurate blood vessel structure in terms of position and diameter is yet an another critical and challenging issue to deal with, so that way our method is simple.…”

Section: Discussionmentioning

confidence: 83%

“…Rangayyan et al (2010) detected optic disc using vessels merging by Gabor filters and phase likeness modeling and achieved a success rate of 100% on DRIVE dataset and 69.1% for STARE datasets. Ashame et al (2018) extracted OD by recognizing optic nerve surrounded with vessels by applying proposed method on DIAETDB1 and DIARETDB0 and achieved sensitivity of 0.92 and 0.91, respectively.…”

Section: Od Detectionmentioning

confidence: 99%

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Automated macula proximity diagnosis for early finding of diabetic macular edema

Patil

2020

Res. Biomed. Eng.

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Purpose Diabetic retinopathy (DR) is non-recoverable in nature. One of the advanced sight threatening condition in diabetic patient is defined in terms of diabetic macular edema (DME), where the macula gets deposited with fluid rich in proteins called exudates. It is indisputably required to find and treat occurrence of exudates near macula in time, to avoid further complications of retina and vision loss at later stage. However, presence of various dark and bright lesions awakes the need of reliable macula and exudate detection process. Proposed work intends to present a robust, scale, and rotation invariant tool for early diagnosis of DME. Methods Optic disc (OD) diameter is one of the important parameters; it is always proportional to the size of the retina; an adaptive method based on pixel count and histogram-based segmentation is used for optic disc detection. Center of macula is detected based on estimated optic disc radius calculations and anatomical priors. In order to reduce computational burden, two disc diameter (DD) region near macula is extracted. Hard exudates are extracted with unique combination of CLAHE and morphological operations, followed by Kirsch's mask for sharp edge detection of hard exudates, and further morphological operations for noise removal. Results OD detection algorithm is evaluated on four different databases DiaretDB1, MESSIDOR, DRIONS-DB, and images from local hospital, overall sensitivity of 95.71% is achieved, highest sensitivity value is 98%, and is achieved on first 100 images from MESSIDOR database. Further DiaretDB1 and images from local hospital are processed further for macula detection and exudates finding in macular proximity, as DiaretDB1 is rich in variety of DR lesions and images from the local hospital to deal with real-time issues. Finding macula region is achieved with an overall success rate of 91.93% and overall sensitivity of 99% is reported on both the chosen databases. Conclusion Proposed work provides an automated tool, for early diagnosis of diabetic macular edema. The method is straightforward, robust and computationally less complex and improved success rates for feature, and lesion detections are achieved as compared with state-of-the-art methods.

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Section: Discussionmentioning

confidence: 83%

Section: Od Detectionmentioning

confidence: 99%

Automated macula proximity diagnosis for early finding of diabetic macular edema

Patil

2020

Res. Biomed. Eng.

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“…The created algorithm integrates pattern recognition with BGR. Laurine A. Ashame et al [12] proposed a system for detecting eye abnormalities using optical disc segments in the fundus image. Adaptive Otsu's Threshold, Hough transform, and Kirsch's template filtering are incorporated into the proposed model.…”

Section: IImentioning

confidence: 99%

Early Detection of Eye Disease Using CNN

Erdin¹,

Patel²

2023

IJRASET

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The eyes perform an essential part of life. The eye is one of the organs that assist humans learn about their natural environments and collect information from them. Almost everywhere in the world, the frequency of eye disease has increased, needing a serious response. Immediate eye detection will be invaluable assistance in offering further treatment to prevent blindness. In this study, a Convolution Neural Network model is used for identifying eye diseases. This research aims to categorize human eyes into four categories: trachoma, conjunctivitis, cataract, and healthy. The investigation got an accuracy of 88.36%, while the CNN model evaluation provided Precision of 89.25%, Recall of 88.75%, and F1 Score of 88.5%. Based on the accuracy and evaluation results, this system can be used for the early detection of multiple eye diseases. Several random samples were also used for testing in this investigation. The test results indicate that this system is functional.

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“…This paper about to propose a method for retina blood vessel extraction to help the specialists in analyse, diagnose and give treatment to the patient with various retinal diseases [5]. To achieve this purpose, the extraction method needs to be accurate and reliable [6]. The proposed method uses the Kirsch template as the method for extracting the blood vessel.…”

Section: Introductionmentioning

confidence: 99%

Retina blood vessel extraction based on kirsch’s template method

Zolkifli

Nazari

Mustafa

et al. 2020

IJEECS

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<p class="IJASEITAbtract">Analysis on the retina blood vessels from fundus images have been widely used in the medical community to detect the disorder condition in the blood vessels. An automated tracing of retina blood vessel can help to provide valuable computer-assisted diagnosis for the ophthalmic disorders. Thus, it helps to reduce the time for the ophthalmologist to analyses and diagnose the result of the fundus image of patient. The purpose of this research is to build an algorithm to trace the retina blood vessels. The method to be used in this research consist of two parts which are the pre-processing part and the feature extraction by using the Kirsch’s template. Combining the pre-processing at the early stage and feature extraction at the next stage is applied to extract the edges of the blood vessels. The proposed algorithm was verified by using two online databases, DRIVE and HRF to validate the performance measures. Hence, proposed method is capable to extract the retina blood vessel and give the accuracy of 0.7917, the sensitivity of 0.9077 and the specificity of 0.7215. In conclusion, the extraction of the blood vessels is highly recommended as the early screening stage for the eye diseases beneficially.</p>

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Abnormality Detection in Eye Fundus Retina

Cited by 7 publications

References 15 publications

Automated macula proximity diagnosis for early finding of diabetic macular edema

Automated macula proximity diagnosis for early finding of diabetic macular edema

Early Detection of Eye Disease Using CNN

Retina blood vessel extraction based on kirsch’s template method

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