Identification of suitable fundus images using automated quality assessment methods

Şevik, Uğur; Köse, Cemal; Berber, Tolga; Erdöl, Hidayet

doi:10.1117/1.jbo.19.4.046006

Cited by 94 publications

(89 citation statements)

References 34 publications

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“…There are several publicly available RIQA datasets with manual quality annotations, such as HRF [7], DRIMDB [13], and DR2 [11]. However, they have various drawbacks.…”

Section: Eye-quality Datasetmentioning

confidence: 99%

Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces

Wang

Shen

et al. 2019

Lecture Notes in Computer Science

143

145

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Retinal image quality assessment (RIQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists or automated analysis systems. Existing RIQA methods focus on the RGB color-space and are developed based on small datasets with binary quality labels (i.e., 'Accept' and 'Reject'). In this paper, we first re-annotate an Eye-Quality (EyeQ) dataset with 28,792 retinal images from the EyePACS dataset, based on a three-level quality grading system (i.e., 'Good', 'Usable' and 'Reject') for evaluating RIQA methods. Our RIQA dataset is characterized by its large-scale size, multi-level grading, and multi-modality. Then, we analyze the influences on RIQA of different color-spaces, and propose a simple yet efficient deep network, named Multiple Color-space Fusion Network (MCF-Net), which integrates the different color-space representations at both a feature-level and prediction-level to predict image quality grades. Experiments on our EyeQ dataset show that our MCF-Net obtains a state-of-the-art performance, outperforming the other deep learning methods. Furthermore, we also evaluate diabetic retinopathy (DR) detection methods on images of different quality, and demonstrate that the performances of automated diagnostic systems are highly dependent on image quality.

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“…There are several publicly available RIQA datasets with manual quality annotations, such as HRF [7], DRIMDB [13], and DR2 [11]. However, they have various drawbacks.…”

Section: Eye-quality Datasetmentioning

confidence: 99%

Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces

Wang

Shen

et al. 2019

Lecture Notes in Computer Science

143

145

View full text Add to dashboard Cite

show abstract

“…Recently, hybrid techniques are being adopted in literature that combine both generic and structural features for RIQA. 7,28,30 Moreover, an interesting emerging approach for sharpness assessment compares retinal images to their blurred versions based on the intuition that unsharp images will be more similar to their blurred versions. 9,16,31…”

Section: Spatial Retinal Image Quality Assessmentmentioning

confidence: 99%

“…Then, several distances related to these structures were measured to assess the image's field of view (FOV). Other algorithms relied only on validating the presence of the OD, 43 fovea, 9 or both 30 in their expected locations. As for outlier detection, Giancardo et al 19 and Sevik et al 30 used RGB information for the identification of outliers.…”

Section: Content Literature Reviewmentioning

confidence: 99%

“…Other algorithms relied only on validating the presence of the OD, 43 fovea, 9 or both 30 in their expected locations. As for outlier detection, Giancardo et al 19 and Sevik et al 30 used RGB information for the identification of outliers. Yin et al 31 used a two-stage approach to detect outlier images in which a bag of words approach first identified nonretinal images.…”

Section: Content Literature Reviewmentioning

confidence: 99%

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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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“…method though, hardly accounts for the loss of vascular details. [4] Some of the other work related to retinal image processing includes the bowler hat transform a new multiscale vessel enhancement approach which is a mathematical morphology has been proposed. [10] The method combines different structural element to detect the vessels.Another method proposed is Contrast Limited Adaptive Histogram Equalization (CLAHE) for preprocessing and Tandem Pulse Coupled Neural Network (TPCNN) model for automatic feature vectors generation then classification and extraction of the retinal blood vessels based on Deep Learning Based Support Vector Machine (DLBSVM).…”

Section: Fig11:mask Imagementioning

confidence: 99%

Retinal Image Enhancement Based on Contrast, Luminosity Adjustment and MSC

2019

IJRTE

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Retinal images have been widely used by ophthalmologists for detecting the retinal diseases before-hand and diagnosing them suitably. Old age macular degeneration, diabetic retinopathy and glaucoma are some examples of these diseases. However, poor quality of the image due to inadvertent circumstances limits the ability of the ophthalmologists to study the image. This paper hereby proposes an algorithm that is used to obtain clearer images by performing contrast and luminosity adjustment that enhances the basic quality of the clicked image. Following this, Multi-dictionary Sparse Coding (MSC) is carried out on the image to obtain the retinal vessel structures and miniscule details. Amount of Image enhancement is calculated by measuring the improvement after each stage of operation on the image. The image's quality is found to be much better compared to the other methods and thus can be suggested to the ophthalmologists for conducting the further medical studies conveniently.

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Identification of suitable fundus images using automated quality assessment methods

Cited by 94 publications

References 34 publications

Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces

Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces

Retinal image quality assessment based on image clarity and content

Retinal Image Enhancement Based on Contrast, Luminosity Adjustment and MSC

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