Quality Assessment of Retinal Fundus Images using Elliptical Local Vessel Density

Giancardo, Luca; Mériaudeau, Fabrice; Karnowski, Thomas P.; Chaum, Edward; Tobin, Kenneth W.

doi:10.5772/7618

Cited by 27 publications

(17 citation statements)

References 36 publications

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“…68,69 For example, pigmentation variations among ethnic groups can affect the observed ocular image. 70 Giancardo et al 71 have noticed that color retinal images of Caucasians have a strong red component whereas those of African Americans had a much stronger blue component. This could have an effect on the proposed retinal saturation channel when dealing with different ethnic groups.…”

Section: Illumination and Homogeneity Algorithms Analysismentioning

confidence: 99%

Retinal image quality assessment based on image clarity and content

et al. 2016

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Retinal image quality assessment (RIQA) is an essential step in automated screening systems to avoid misdiagnosis caused by processing poor quality retinal images. A no-reference transform-based RIQA algorithm is introduced that assesses images based on five clarity and content quality issues: sharpness, illumination, homogeneity, field definition, and content. Transform-based RIQA algorithms have the advantage of considering retinal structures while being computationally inexpensive. Wavelet-based features are proposed to evaluate the sharpness and overall illumination of the images. A retinal saturation channel is designed and used along with wavelet-based features for homogeneity assessment. The presented sharpness and illumination features are utilized to assure adequate field definition, whereas color information is used to exclude nonretinal images. Several publicly available datasets of varying quality grades are utilized to evaluate the feature sets resulting in area under the receiver operating characteristic curve above 0.99 for each of the individual feature sets. The overall quality is assessed by a classifier that uses the collective features as an input vector. The classification results show superior performance of the algorithm in comparison to other methods from literature. Moreover, the algorithm addresses efficiently and comprehensively various quality issues and is suitable for automatic screening systems.

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Section: Illumination and Homogeneity Algorithms Analysismentioning

confidence: 99%

Retinal image quality assessment based on image clarity and content

et al. 2016

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“…The MESSIDOR [24] dataset containing 80 fundus images with exudates region with Zeiss Visucam PRO fundus camera, at resolution of 1449x2201 pixel and with a 45° Field of view. The image capturing process is vetted by automatic quality assessment algorithms based on the Elliptical local vasculature density feature [25][26].…”

Section: Methodsmentioning

confidence: 99%

Designing of Computer Aided Diagnostic System for the Identification of Exudates in Retinal Fundus Images

Mishra

Mittal

Sunkaria

2015

JBEMi

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Macular edema is an advance stage of diabetic retinopathy which affects central vision of diabetes patients. The main cause of edema is the appearance of exudates near or on macular region in human retina. If the exudates are present in the macular region of retina, it will lead to diabetic macular edema. Early detection of macular edema in diabetic patients paves a path for prevention from blindness. The automatic system for early detection of diabetic macular edema should identify all possible exudates present on the surface of retina. In the proposed work, a computer added diagnosis system is design for the identification of the exudates in color retinal fundus images. The system consists of three stages; candidate exudates detection, feature extraction and classification. The system is designed with (i) background estimation, morphological reconstruction, normalization for candidate exudates detection (ii) Gray level co-occurrence matrix for feature extraction and (iii) support vector machine for classification. The classifier classifies in between the region of exudates and non-exudates. The system performance is evaluated in terms of the parameters such as sensitivity, specificity, mathews correlation coefficient, positive predicative value, and accuracy whose values are 88.23%,

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“…[6][7][8][9][10] A shortcoming of local analysis is the processing time that, usually, is longer than in global techniques. Segmentation of retinal features such as optic disc, fovea, and retinal vasculature is also included in some methods to augment specificity of the algorithms to fundus images.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies present the combination of structural parameters with generic parameters in their systems. 9,10,14 The automatic analysis of medical images with low quality can increase the false negatives. 17 As a consequence, the development of a system to analyze the image quality of fundus images has been recognized by the majority of researchers, working in the field, as a way to improve and guarantee that images present minimum quality.…”

Section: Introductionmentioning

confidence: 99%

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Quality evaluation of digital fundus images through combined measures

Veiga

Pereira²,

Ferreira

et al. 2014

J. Med. Imag

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Abstract. The evaluation of image quality is an important step before an automatic analysis of retinal images. Several conditions can impair the acquisition of a good image, and minimum image quality requirements should be present to ensure that an automatic or semiautomatic system provides an accurate diagnosis. A method to classify fundus images as low or good quality is presented. The method starts with the detection of regions of uneven illumination and evaluates if the segmented noise masks affect a clinically relevant area (around the macula). Afterwards, focus is evaluated through a fuzzy classifier. An input vector is created extracting three focus features. The system was validated in a large dataset (1454 fundus images), obtained from an online database and an eye clinic and compared with the ratings of three observers. The system performance was close to optimal with an area under the receiver operating characteristic curve of 0.9943.

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Quality Assessment of Retinal Fundus Images using Elliptical Local Vessel Density

Cited by 27 publications

References 36 publications

Retinal image quality assessment based on image clarity and content

Retinal image quality assessment based on image clarity and content

Designing of Computer Aided Diagnostic System for the Identification of Exudates in Retinal Fundus Images

Quality evaluation of digital fundus images through combined measures

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