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Lyu X, Jajal P, Tahir MZ, Zhang S. Fractal dimension of retinal vasculature as an image quality metric for automated fundus image analysis systems. Sci Rep 2022; 12:11868. [PMID: 35831401 PMCID: PMC9279448 DOI: 10.1038/s41598-022-16089-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 07/04/2022] [Indexed: 11/21/2022] Open
Abstract
Automated fundus screening is becoming a significant programme of telemedicine in ophthalmology. Instant quality evaluation of uploaded retinal images could decrease unreliable diagnosis. In this work, we propose fractal dimension of retinal vasculature as an easy, effective and explainable indicator of retinal image quality. The pipeline of our approach is as follows: utilize image pre-processing technique to standardize input retinal images from possibly different sources to a uniform style; then, an improved deep learning empowered vessel segmentation model is employed to extract retinal vessels from the pre-processed images; finally, a box counting module is used to measure the fractal dimension of segmented vessel images. A small fractal threshold (could be a value between 1.45 and 1.50) indicates insufficient image quality. Our approach has been validated on 30,644 images from four public database.
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Affiliation(s)
- Xingzheng Lyu
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, China.
| | - Purvish Jajal
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6G 1H9, Canada
| | - Muhammad Zeeshan Tahir
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, China
| | - Sanyuan Zhang
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, China
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Evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy. Sci Rep 2021; 11:16641. [PMID: 34404857 PMCID: PMC8371000 DOI: 10.1038/s41598-021-96068-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 07/19/2021] [Indexed: 11/08/2022] Open
Abstract
Adaptive optics flood illumination ophthalmoscopy (AO-FIO) is an established imaging tool in the investigation of retinal diseases. However, the clinical interpretation of AO-FIO images can be challenging due to varied image quality. Therefore, image quality assessment is essential before interpretation. An image assessment tool will also assist further work on improving the image quality, either during acquisition or post processing. In this paper, we describe, validate and compare two automated image quality assessment methods; the energy of Laplacian focus operator (LAPE; not commonly used but easily implemented) and convolutional neural network (CNN; effective but more complex approach). We also evaluate the effects of subject age, axial length, refractive error, fixation stability, disease status and retinal location on AO-FIO image quality. Based on analysis of 10,250 images of 50 × 50 μm size, at 41 retinal locations, from 50 subjects we demonstrate that CNN slightly outperforms LAPE in image quality assessment. CNN achieves accuracy of 89%, whereas LAPE metric achieves 73% and 80% (for a linear regression and random forest multiclass classifier methods, respectively) compared to ground truth. Furthermore, the retinal location, age and disease are factors that can influence the likelihood of poor image quality.
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Li Z, Guo C, Nie D, Lin D, Zhu Y, Chen C, Zhao L, Wu X, Dongye M, Xu F, Jin C, Zhang P, Han Y, Yan P, Lin H. Deep learning from "passive feeding" to "selective eating" of real-world data. NPJ Digit Med 2020; 3:143. [PMID: 33145439 PMCID: PMC7603327 DOI: 10.1038/s41746-020-00350-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 09/24/2020] [Indexed: 12/23/2022] Open
Abstract
Artificial intelligence (AI) based on deep learning has shown excellent diagnostic performance in detecting various diseases with good-quality clinical images. Recently, AI diagnostic systems developed from ultra-widefield fundus (UWF) images have become popular standard-of-care tools in screening for ocular fundus diseases. However, in real-world settings, these systems must base their diagnoses on images with uncontrolled quality ("passive feeding"), leading to uncertainty about their performance. Here, using 40,562 UWF images, we develop a deep learning-based image filtering system (DLIFS) for detecting and filtering out poor-quality images in an automated fashion such that only good-quality images are transferred to the subsequent AI diagnostic system ("selective eating"). In three independent datasets from different clinical institutions, the DLIFS performed well with sensitivities of 96.9%, 95.6% and 96.6%, and specificities of 96.6%, 97.9% and 98.8%, respectively. Furthermore, we show that the application of our DLIFS significantly improves the performance of established AI diagnostic systems in real-world settings. Our work demonstrates that "selective eating" of real-world data is necessary and needs to be considered in the development of image-based AI systems.
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Affiliation(s)
- Zhongwen Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, 510060 Guangzhou, China
| | - Chong Guo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, 510060 Guangzhou, China
| | - Danyao Nie
- Shenzhen Eye Hospital, Shenzhen Key Laboratory of Ophthalmology, Affiliated Shenzhen Eye Hospital of Jinan University, 518001 Shenzhen, China
| | - Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, 510060 Guangzhou, China
| | - Yi Zhu
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL 33136 USA
| | - Chuan Chen
- Sylvester Comprehensive Cancer Centre, University of Miami Miller School of Medicine, Miami, FL 33136 USA
| | - Lanqin Zhao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, 510060 Guangzhou, China
| | - Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, 510060 Guangzhou, China
| | - Meimei Dongye
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, 510060 Guangzhou, China
| | - Fabao Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, 510060 Guangzhou, China
| | - Chenjin Jin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, 510060 Guangzhou, China
| | - Ping Zhang
- Xudong Ophthalmic Hospital, 015000 Inner Mongolia, China
| | - Yu Han
- EYE and ENT Hospital of Fudan University, 200031 Shanghai, China
| | - Pisong Yan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, 510060 Guangzhou, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, 510060 Guangzhou, China
- Centre for Precision Medicine, Sun Yat-sen University, 510060 Guangzhou, China
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Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine. J Digit Imaging 2019; 31:869-878. [PMID: 29704086 DOI: 10.1007/s10278-018-0084-9] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022] Open
Abstract
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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Passaglia CL, Arvaneh T, Greenberg E, Richards D, Madow B. Automated Method of Grading Vitreous Haze in Patients With Uveitis for Clinical Trials. Transl Vis Sci Technol 2018; 7:10. [PMID: 29600118 PMCID: PMC5868860 DOI: 10.1167/tvst.7.2.10] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2017] [Accepted: 02/11/2018] [Indexed: 12/29/2022] Open
Abstract
Purpose Uveitis is associated with accumulation of exudate in the vitreous, which reduces fundus visibility. The condition is assessed in patients by subjectively matching fundus photographs to a six-level (NIH) or nine-level (Miami) haze scale. This study aimed to develop an objective method of assessing vitreous haze. Methods An image-processing algorithm was designed that quantifies vitreous haze via high-pass filtering, entropy analysis, and power spectrum integration. The algorithm was refined using nine published photographs that represent incremental levels of fundus blur and applied without further refinement to 120 random fundus photographs from a uveitis image library. Computed scores were compared against the grades of two trained readers of vitreous haze and against acutance, a generic measure of image clarity, using Cohen's κ and Gwet's AC statistics. Results Exact agreement between algorithm scores and reader grades was substantial for both NIH and Miami scales (κ = 0.61 and 0.67, AC = 0.82 and 0.92). Within-one (κ = 0.78 and 0.82) and within-two (κ = 0.80 and 0.84) levels of agreement were almost perfect. The correspondence was comparable to that between readers. Whereas, exact (κ = 0.45 and 0.44, AC = 0.73 and 0.75), within-one (κ = 0.69 and 0.68), and within-two (κ = 0.73 and 0.72) levels of agreement for the two scales were moderate to substantial for acutance calculations. Conclusions The computer algorithm produces a quantitative measure of vitreous haze that correlates strongly with the perception of expert graders. Translational Relevance The work offers a rapid, unbiased, standardized means of assessing vitreous haze for clinical and telemedical monitoring of uveitis patients.
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Affiliation(s)
- Christopher L Passaglia
- Department of Chemical and Biomedical Engineering, University of South Florida, Tampa, FL, USA.,Department of Ophthalmology, University of South Florida, Tampa, FL, USA
| | - Tia Arvaneh
- Department of Chemical and Biomedical Engineering, University of South Florida, Tampa, FL, USA
| | - Erin Greenberg
- Department of Ophthalmology, University of South Florida, Tampa, FL, USA
| | - David Richards
- Department of Ophthalmology, University of South Florida, Tampa, FL, USA
| | - Brian Madow
- Department of Ophthalmology, University of South Florida, Tampa, FL, USA
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Abdel-Hamid L, El-Rafei A, El-Ramly S, Michelson G, Hornegger J. Retinal image quality assessment based on image clarity and content. JOURNAL OF BIOMEDICAL OPTICS 2016; 21:96007. [PMID: 27637005 DOI: 10.1117/1.jbo.21.9.096007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 08/29/2016] [Indexed: 06/06/2023]
Abstract
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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Affiliation(s)
- Lamiaa Abdel-Hamid
- Misr International University, Department of Electronics and Communication, Faculty of Engineering, Ismalia Road km28, Cairo, Egypt
| | - Ahmed El-Rafei
- Ain Shams University, Department of Engineering Physics and Mathematics, Faculty of Engineering, 1 El-Sarayat Street, Abbasia, Cairo 11517, Egypt
| | - Salwa El-Ramly
- Ain Shams University, Department of Electronics and Communication, Faculty of Engineering, 1 El-Sarayat Street, Abbasia, Cairo 11517, Egypt
| | - Georg Michelson
- Friedrich-Alexander University of Erlangen-Nuremberg, Department of Ophthalmology, Schwabachanlage 6, Erlangen 91054, GermanyeTalkingeyes & More GmbH, Medical Valley Center, Erlangen 91052, Germany
| | - Joachim Hornegger
- Friedrich-Alexander University of Erlangen-Nuremberg, Pattern Recognition Lab, Department of Computer Science, Martensstr. 3, Erlangen 91058, Germany
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Welikala R, Fraz M, Foster P, Whincup P, Rudnicka A, Owen C, Strachan D, Barman S. Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies. Comput Biol Med 2016; 71:67-76. [DOI: 10.1016/j.compbiomed.2016.01.027] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Revised: 01/14/2016] [Accepted: 01/30/2016] [Indexed: 12/01/2022]
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Trucco E, Ruggeri A, Karnowski T, Giancardo L, Chaum E, Hubschman JP, Al-Diri B, Cheung CY, Wong D, Abràmoff M, Lim G, Kumar D, Burlina P, Bressler NM, Jelinek HF, Meriaudeau F, Quellec G, Macgillivray T, Dhillon B. Validating retinal fundus image analysis algorithms: issues and a proposal. Invest Ophthalmol Vis Sci 2013; 54:3546-59. [PMID: 23794433 DOI: 10.1167/iovs.12-10347] [Citation(s) in RCA: 119] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
This paper concerns the validation of automatic retinal image analysis (ARIA) algorithms. For reasons of space and consistency, we concentrate on the validation of algorithms processing color fundus camera images, currently the largest section of the ARIA literature. We sketch the context (imaging instruments and target tasks) of ARIA validation, summarizing the main image analysis and validation techniques. We then present a list of recommendations focusing on the creation of large repositories of test data created by international consortia, easily accessible via moderated Web sites, including multicenter annotations by multiple experts, specific to clinical tasks, and capable of running submitted software automatically on the data stored, with clear and widely agreed-on performance criteria, to provide a fair comparison.
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Affiliation(s)
- Emanuele Trucco
- VAMPIRE project, School of Computing, University of Dundee, Dundee, United Kingdom.
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Hunter A, Lowell JA, Ryder B, Basu A, Steel D. Automated diagnosis of referable maculopathy in diabetic retinopathy screening. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:3375-8. [PMID: 22255063 DOI: 10.1109/iembs.2011.6090914] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper introduces an algorithm for the automated diagnosis of referable maculopathy in retinal images for diabetic retinopathy screening. Referable maculopathy is a potentially sight-threatening condition requiring immediate referral to an ophthalmologist from the screening service, and therefore accurate referral is extremely important. The algorithm uses a pipeline of detection and filtering of "peak points" with strong local contrast, segmentation of candidate lesions, extraction of features and classification by a multilayer perceptron. The optic nerve head and fovea are detected, so that the macula region can be identified and scanned. The algorithm is assessed against a reference standard database drawn from the Birmingham City Hospital (UK) diabetic retinopathy screening programme, against two possible modes of use: independent screening, and pre-filtering to reduce human screener workload.
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Dias JMP, Oliveira CM, Cruz LADS. Evaluation of Retinal Image Gradability by Image Features Classification. ACTA ACUST UNITED AC 2012. [DOI: 10.1016/j.protcy.2012.09.096] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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