Diabetic eye sentinel: prescreening of diabetic retinopathy using retinal images obtained by a mobile phone camera

Author(s):  
Thayanee Ruennak ◽  
Pakinee Aimmanee ◽  
Stanislav Makhanov ◽  
Navapol Kanchanaranya ◽  
Sakchai Vongkittirux
2020 ◽  
Vol 52 (4) ◽  
pp. 726-732
Author(s):  
Claire Beaugrand

In a tweet posted on 29 March 2018, a bidūn activist—who was later jailed from July 2019 to January 2020 for peacefully protesting against the inhumane conditions under which the bidūn are living—shared a video. The brief video zooms in closely on an ID card, recognizable as one of those issued to the bidūn, or long-term residents of Kuwait who are in contention with the state regarding their legal status. More precisely, the mobile phone camera focuses on the back of the ID card, on one line with a special mention added by the Central System (al-jihāz al-markazī), the administration in charge of bidūn affairs. Other magnetic strip cards hide the personal data written above and below it. A male voice can be heard saying that he will read this additional remark, but before even doing so he bursts into laughter. The faceless voice goes on to read out the label in an unrestrained laugh: “ladayh qarīb … ladayh qarīna … dālla ʿalā al-jinsiyya al-ʿIrāqiyya” (he has a relative … who has presumptive evidence … suggesting an Iraqi nationality). The video shakes as the result of a contagious laugh that grows in intensity. In the Kuwaiti dialect, the voice continues commenting: “Uqsim bil-Allāh, gaʿadt sāʿa ufakkir shinū maʿanāt hal-ḥatchī” (I swear by God, it took me an hour to figure out the meaning of this nonsense), before reading the sentence again, stopping and guffawing, and asking if he should “repeat it a third time,” expressing amazement at its absurdity. The tweet, addressed to the head of the Central System (mentioned in the hashtag #faḍīḥat Sāliḥ al-Faḍāla, or #scandal Salih al-Fadala), reads: In lam tastaḥī fa-'ktub mā shaʾt (Don't bother, write what you want).


IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 53053-53061 ◽  
Author(s):  
Zhenshan Zhang ◽  
Tiantian Zhang ◽  
Ji Zhou ◽  
Yueming Lu ◽  
Yaojun Qiao

2020 ◽  
Vol 4 (2) ◽  
pp. 53-60
Author(s):  
Latifah Listyalina ◽  
Yudianingsih Yudianingsih ◽  
Dhimas Arief Dharmawan

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.


2019 ◽  
Vol 8 (2S11) ◽  
pp. 3637-3640

Retinal vessels ID means to isolate the distinctive retinal configuration issues, either wide or restricted from fundus picture foundation, for example, optic circle, macula, and unusual sores. Retinal vessels recognizable proof investigations are drawing in increasingly more consideration today because of pivotal data contained in structure which is helpful for the identification and analysis of an assortment of retinal pathologies included yet not restricted to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the advancement of right around two decades, the inventive methodologies applying PC supported systems for portioning retinal vessels winding up increasingly significant and coming nearer. Various kinds of retinal vessels segmentation strategies discussed by using Deep Learning methods. At that point, the pre-processing activities and the best in class strategies for retinal vessels distinguishing proof are presented.


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