Retinal Image Quality Assessment Using Shearlet Transform

Imani, Elaheh; Pourreza, Hamid Reza; Banaee, Touka

doi:10.1007/978-3-319-16727-5_28

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…Several approaches have been developed to automatically determine the quality of the retinal images. These approaches can be divided into two groups based on which image parameters/criteria they consider to classify the image quality [13]. The first group is based on generic image quality parameters such as sharpness and contrast.…”

Section: Review Of the Retinal Image Quality Assessment Methodsmentioning

confidence: 99%

Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine

et al. 2018

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Fundus images obtained in a telemedicine program are acquired at different sites that are captured by people who have varying levels of experience. These result in a relatively high percentage of images which are later marked as unreadable by graders. Unreadable images require a recapture which is time and cost intensive. An automated method that determines the image quality during acquisition is an effective alternative. To determine the image quality during acquisition, we describe here an automated method for the assessment of image quality in the context of diabetic retinopathy. The method explicitly applies machine learning techniques to access the image and to determine 'accept' and 'reject' categories. 'Reject' category image requires a recapture. A deep convolution neural network is trained to grade the images automatically. A large representative set of 7000 colour fundus images was used for the experiment which was obtained from the EyePACS that were made available by the California Healthcare Foundation. Three retinal image analysis experts were employed to categorise these images into 'accept' and 'reject' classes based on the precise definition of image quality in the context of DR. The network was trained using 3428 images. The method shows an accuracy of 100% to successfully categorise 'accept' and 'reject' images, which is about 2% higher than the traditional machine learning method. On a clinical trial, the proposed method shows 97% agreement with human grader. The method can be easily incorporated with the fundus image capturing system in the acquisition centre and can guide the photographer whether a recapture is necessary or not.

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Section: Review Of the Retinal Image Quality Assessment Methodsmentioning

confidence: 99%

Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine

et al. 2018

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“…In retinal images, major edges arise from vasculature, optic disk, and lesions. 13 According to statistics, vasculature and red lesions are always invisible in red planes of retinal photographs while there are little useful information but noise can be obtained from the blue planes. 26 Since each part of fundus and types of lesions can be distinguished in green planes well, only the green plane is used in our study.…”

Section: Proposed Sharpness Metric For Fundus Imagesmentioning

confidence: 99%

“…The clarity of the image can be seen as a function of edge strength. 13 Strength of edges in blurred or low-contrast images is always weak. Edge information can be used to evaluate fundus image sharpness.…”

Section: Introductionmentioning

confidence: 99%

A retinal image sharpness metric based on histogram of edge width

Jia-jun

Weng

Xue

et al. 2017

Journal of Algorithms & Computational Technology

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Retinal image sharpness assessment is one of the critical requirements of automatic quality evaluation in telemedicine screening for diabetic retinopathy. In this paper, a new sharpness metric measuring the spread of edges is presented to quantify fundus image clarity. After edge detection on the region of interest of retinal image, the width of each edge is calculated and the histogram of region of interest generated. Based on the histogram, a distance-based factor is introduced to gain the weighted edge width, which is defined as the sharpness metric for the fundus image. The method was tested on Messidor dataset and a proprietary dataset. The results show that the proposed metric performs well over different image distortion levels and resolutions and is of low computational complexity. The weighted edge width value of gradable retinal image, which is irrelevant to resolution, is always within the range of 3-7 pixels.

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Retinal Image Quality Assessment Using Shearlet Transform

Cited by 2 publications

References 12 publications

Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine

Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine

A retinal image sharpness metric based on histogram of edge width

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