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Xue X, Wang L, Du W, Fujiwara Y, Peng Y. Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images. SENSORS (BASEL, SWITZERLAND) 2022; 22:6899. [PMID: 36146249 PMCID: PMC9506381 DOI: 10.3390/s22186899] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/08/2022] [Accepted: 09/08/2022] [Indexed: 06/16/2023]
Abstract
The accurate segmentation of the optic disc (OD) in fundus images is a crucial step for the analysis of many retinal diseases. However, because of problems such as vascular occlusion, parapapillary atrophy (PPA), and low contrast, accurate OD segmentation is still a challenging task. Therefore, this paper proposes a multiple preprocessing hybrid level set model (HLSM) based on area and shape for OD segmentation. The area-based term represents the difference of average pixel values between the inside and outside of a contour, while the shape-based term measures the distance between a prior shape model and the contour. The average intersection over union (IoU) of the proposed method was 0.9275, and the average four-side evaluation (FSE) was 4.6426 on a public dataset with narrow-angle fundus images. The IoU was 0.8179 and the average FSE was 3.5946 on a wide-angle fundus image dataset compiled from a hospital. The results indicate that the proposed multiple preprocessing HLSM is effective in OD segmentation.
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Affiliation(s)
- Xiaozhong Xue
- Information and Human Science, Kyoto Institute of Technology University, Kyoto 6068585, Japan
| | - Linni Wang
- Retina & Neuron-Ophthalmology, Tianjin Medical University Eye Hospital, Tianjin 300084, China
| | - Weiwei Du
- Information and Human Science, Kyoto Institute of Technology University, Kyoto 6068585, Japan
| | - Yusuke Fujiwara
- Information and Human Science, Kyoto Institute of Technology University, Kyoto 6068585, Japan
| | - Yahui Peng
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
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Onal S, Chen X, Satamraju V, Balasooriya M, Dabil-Karacal H. Automated and simultaneous fovea center localization and macula segmentation using the new dynamic identification and classification of edges model. J Med Imaging (Bellingham) 2016; 3:034002. [PMID: 27660803 DOI: 10.1117/1.jmi.3.3.034002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 08/23/2016] [Indexed: 11/14/2022] Open
Abstract
Detecting the position of retinal structures, including the fovea center and macula, in retinal images plays a key role in diagnosing eye diseases such as optic nerve hypoplasia, amblyopia, diabetic retinopathy, and macular edema. However, current detection methods are unreliable for infants or certain ethnic populations. Thus, a methodology is proposed here that may be useful for infants and across ethnicities that automatically localizes the fovea center and segments the macula on digital fundus images. First, dark structures and bright artifacts are removed from the input image using preprocessing operations, and the resulting image is transformed to polar space. Second, the fovea center is identified, and the macula region is segmented using the proposed dynamic identification and classification of edges (DICE) model. The performance of the method was evaluated using 1200 fundus images obtained from the relatively large, diverse, and publicly available Messidor database. In 96.1% of these 1200 cases, the distance between the fovea center identified manually by ophthalmologists and automatically using the proposed method remained within 0 to 8 pixels. The dice similarity index comparing the manually obtained results with those of the model for macula segmentation was 96.12% for these 1200 cases. Thus, the proposed method displayed a high degree of accuracy. The methodology using the DICE model is unique and advantageous over previously reported methods because it simultaneously determines the fovea center and segments the macula region without using any structural information, such as optic disc or blood vessel location, and it may prove useful for all populations, including infants.
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Affiliation(s)
- Sinan Onal
- Southern Illinois University-Edwardsville , Department of Mechanical and Industrial Engineering, Box 1805, Edwardsville, Illinois 62026, United States
| | - Xin Chen
- Southern Illinois University-Edwardsville , Department of Mechanical and Industrial Engineering, Box 1805, Edwardsville, Illinois 62026, United States
| | - Veeresh Satamraju
- Southern Illinois University-Edwardsville , Department of Electrical and Computer, Box 1801, Edwardsville, Illinois 62026, United States
| | - Maduka Balasooriya
- Southern Illinois University-Edwardsville , Department of Mathematics and Statistics, Box 1805, Edwardsville, Illinois 62026, United States
| | - Humeyra Dabil-Karacal
- Washington University , School of Medicine, Department of Ophthalmology and Visual Sciences, 10 Barnes West Drive Suite 201 C, St. Louis, Missouri 63141, United States
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Hu Q, Abràmoff MD, Garvin MK. Automated construction of arterial and venous trees in retinal images. J Med Imaging (Bellingham) 2015; 2:044001. [PMID: 26636114 DOI: 10.1117/1.jmi.2.4.044001] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Accepted: 09/28/2015] [Indexed: 11/14/2022] Open
Abstract
While many approaches exist to segment retinal vessels in fundus photographs, only a limited number focus on the construction and disambiguation of arterial and venous trees. Previous approaches are local and/or greedy in nature, making them susceptible to errors or limiting their applicability to large vessels. We propose a more global framework to generate arteriovenous trees in retinal images, given a vessel segmentation. In particular, our approach consists of three stages. The first stage is to generate an overconnected vessel network, named the vessel potential connectivity map (VPCM), consisting of vessel segments and the potential connectivity between them. The second stage is to disambiguate the VPCM into multiple anatomical trees, using a graph-based metaheuristic algorithm. The third stage is to classify these trees into arterial or venous (A/V) trees. We evaluated our approach with a ground truth built based on a public database, showing a pixel-wise classification accuracy of 88.15% using a manual vessel segmentation as input, and 86.11% using an automatic vessel segmentation as input.
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Affiliation(s)
- Qiao Hu
- University of Iowa , Department of Electrical and Computer Engineering, 4016 Seamans Center, Iowa City, Iowa 52242, United States
| | - Michael D Abràmoff
- University of Iowa , Department of Electrical and Computer Engineering, 4016 Seamans Center, Iowa City, Iowa 52242, United States ; University of Iowa , Department of Biomedical Engineering, 1402 Seamans Center, Iowa City, Iowa 52242, United States ; University of Iowa , Department of Ophthalmology and Visual Sciences, 200 Hawkins Drive, Iowa City, Iowa 52242, United States ; University of Iowa , Stephen A. Wynn Institute for Vision Research, 200 Hawkins Drive, Iowa City, Iowa 52242, United States
| | - Mona K Garvin
- University of Iowa , Department of Electrical and Computer Engineering, 4016 Seamans Center, Iowa City, Iowa 52242, United States ; Iowa City VA Health Care System , 601 Highway 6 West, Iowa City, Iowa 52246, United States
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Detecting optic disc on asians by multiscale gaussian filtering. Int J Biomed Imaging 2012; 2012:727154. [PMID: 22844267 PMCID: PMC3392893 DOI: 10.1155/2012/727154] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2012] [Revised: 04/23/2012] [Accepted: 04/23/2012] [Indexed: 11/18/2022] Open
Abstract
The optic disc (OD) is an important anatomical feature in retinal images, and its detection is vital for developing automated screening programs. Currently, there is no algorithm designed to automatically detect the OD in fundus images captured from Asians which are larger and have thicker vessels compared to Caucasians. In this paper, we propose such a method to complement current algorithms using two steps: OD vessel candidate detection and OD vessel candidate matching. The first step is achieved with multiscale Gaussian filtering, scale production, and double thresholding to initially extract the vessels' directional map of various thicknesses. The map is then thinned before another threshold is applied to remove pixels with low intensities. This result forms the OD vessel candidates. In the second step, a Vessels' Directional Matched Filter (VDMF) of various dimensions is applied to the candidates to be matched, and the pixel with the smallest difference designated the OD center. We tested the proposed method on a new database consisting of 402 images from a diabetic retinopathy (DR) screening programme consisting of Asians. The OD center was successfully detected with an accuracy of 99.25% (399/402).
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Tang L, Garvin MK, Lee K, Alward WL, Kwon YH, Abràmoff MD. Robust multiscale stereo matching from fundus images with radiometric differences. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2011; 33:2245-2258. [PMID: 21464502 PMCID: PMC3580181 DOI: 10.1109/tpami.2011.69] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
A robust multiscale stereo matching algorithm is proposed to find reliable correspondences between low contrast and weakly textured retinal image pairs with radiometric differences. Existing algorithms designed to deal with piecewise planar surfaces with distinct features and Lambertian reflectance do not apply in applications such as 3D reconstruction of medical images including stereo retinal images. In this paper, robust pixel feature vectors are formulated to extract discriminative features in the presence of noise in scale space, through which the response of low-frequency mechanisms alter and interact with the response of high-frequency mechanisms. The deep structures of the scene are represented with the evolution of disparity estimates in scale space, which distributes the matching ambiguity along the scale dimension to obtain globally coherent reconstructions. The performance is verified both qualitatively by face validity and quantitatively on our collection of stereo fundus image sets with ground truth, which have been made publicly available as an extension of standard test images for performance evaluation.
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Affiliation(s)
- Li Tang
- Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA 52242
| | - Mona K. Garvin
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242
| | - Kyungmoo Lee
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242
| | - Wallace L.M. Alward
- Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA 52242
| | - Young H. Kwon
- Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA 52242
| | - Michael D. Abràmoff
- Department of Ophthalmology and Visual Sciences, the Department of Electrical and Computer Engineering, and the Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, and with the Veteran’s Administration Medical Center, Iowa City, IA 52240
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Abramoff MD, Niemeijer M, Russell SR. Automated detection of diabetic retinopathy: barriers to translation into clinical practice. Expert Rev Med Devices 2010; 7:287-96. [PMID: 20214432 DOI: 10.1586/erd.09.76] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Automated identification of diabetic retinopathy (DR), the primary cause of blindness and visual loss for those aged 18-65 years, from color images of the retina has enormous potential to increase the quality, cost-effectiveness and accessibility of preventative care for people with diabetes. Through advanced image analysis techniques, retinal images are analyzed for abnormalities that define and correlate with the severity of DR. Translating automated DR detection into clinical practice will require surmounting scientific and nonscientific barriers. Scientific concerns, such as DR detection limits compared with human experts, can be studied and measured. Ethical, legal and political issues can be addressed, but are difficult or impossible to measure. The primary objective of this review is to survey the methods, potential benefits and limitations of automated detection in order to better manage translation into clinical practice, based on extensive experience with the systems we have developed.
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Affiliation(s)
- Michael D Abramoff
- Department of Ophthalmology and Visual Sciences, University of Iowa, 11290C PFP UIHC, 200 Hawkins Drive, Iowa City, IA 52242, USA.
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Abràmoff MD, Reinhardt JM, Russell SR, Folk JC, Mahajan VB, Niemeijer M, Quellec G. Automated early detection of diabetic retinopathy. Ophthalmology 2010; 117:1147-54. [PMID: 20399502 PMCID: PMC2881172 DOI: 10.1016/j.ophtha.2010.03.046] [Citation(s) in RCA: 148] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2009] [Revised: 03/18/2010] [Accepted: 03/19/2010] [Indexed: 11/16/2022] Open
Abstract
PURPOSE To compare the performance of automated diabetic retinopathy (DR) detection, using the algorithm that won the 2009 Retinopathy Online Challenge Competition in 2009, the Challenge2009, against that of the one currently used in EyeCheck, a large computer-aided early DR detection project. DESIGN Evaluation of diagnostic test or technology. PARTICIPANTS Fundus photographic sets, consisting of 2 fundus images from each eye, were evaluated from 16670 patient visits of 16,670 people with diabetes who had not previously been diagnosed with DR. METHODS The fundus photographic set from each visit was analyzed by a single retinal expert; 793 of the 16,670 sets were classified as containing more than minimal DR (threshold for referral). The outcomes of the 2 algorithmic detectors were applied separately to the dataset and were compared by standard statistical measures. MAIN OUTCOME MEASURES The area under the receiver operating characteristic curve (AUC), a measure of the sensitivity and specificity of DR detection. RESULTS Agreement was high, and examination results indicating more than minimal DR were detected with an AUC of 0.839 by the EyeCheck algorithm and an AUC of 0.821 for the Challenge2009 algorithm, a statistically nonsignificant difference (z-score, 1.91). If either of the algorithms detected DR in combination, the AUC for detection was 0.86, the same as the theoretically expected maximum. At 90% sensitivity, the specificity of the EyeCheck algorithm was 47.7% and that of the Challenge2009 algorithm was 43.6%. CONCLUSIONS Diabetic retinopathy detection algorithms seem to be maturing, and further improvements in detection performance cannot be differentiated from best clinical practices, because the performance of competitive algorithm development now has reached the human intrareader variability limit. Additional validation studies on larger, well-defined, but more diverse populations of patients with diabetes are needed urgently, anticipating cost-effective early detection of DR in millions of people with diabetes to triage those patients who need further care at a time when they have early rather than advanced DR.
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Affiliation(s)
- Michael D Abràmoff
- Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, Iowa 52242, USA
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Abràmoff MD, Lee K, Niemeijer M, Alward WLM, Greenlee EC, Garvin MK, Sonka M, Kwon YH. Automated segmentation of the cup and rim from spectral domain OCT of the optic nerve head. Invest Ophthalmol Vis Sci 2009; 50:5778-84. [PMID: 19608531 DOI: 10.1167/iovs.09-3790] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
PURPOSE To evaluate the performance of an automated algorithm for determination of the cup and rim from close-to-isotropic spectral domain (SD) OCT images of the optic nerve head (ONH) and compare to the cup and rim as determined by glaucoma experts from stereo color photographs of the same eye. METHODS Thirty-four consecutive patients with glaucoma were included in the study, and the ONH in the left eye was imaged with SD-OCT and stereo color photography on the same day. The cup and rim were segmented in all ONH OCT volumes by a novel voxel column classification algorithm, and linear cup-to-disc (c/d) ratio was determined. Three fellowship-trained glaucoma specialists performed planimetry on the stereo color photographs, and c/d was also determined. The primary outcome measure was the correlation between algorithm-determined c/d and planimetry-derived c/d. RESULTS The correlation of algorithm c/d to experts 1, 2, and 3 was 0.90, 0.87, and 0.93, respectively. The c/d correlation of expert 1 to 2, 1 to 3, and 2 to 3, were 0.89, 0.93, and 0.88, respectively. CONCLUSIONS In this preliminary study, we have developed a novel algorithm to determine the cup and rim in close-to-isotropic SD-OCT images of the ONH and have shown that its performance for determination of the cup and rim from SD-OCT images is similar to that of planimetry by glaucoma experts. Validation on a larger glaucoma sample as well as normal controls is warranted.
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Affiliation(s)
- Michael D Abràmoff
- Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, Iowa 52242, USA.
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Niemeijer M, Abramoff MD, van Ginneken B. Information fusion for diabetic retinopathy CAD in digital color fundus photographs. IEEE TRANSACTIONS ON MEDICAL IMAGING 2009; 28:775-785. [PMID: 19150786 DOI: 10.1109/tmi.2008.2012029] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The purpose of computer-aided detection or diagnosis (CAD) technology has so far been to serve as a second reader. If, however, all relevant lesions in an image can be detected by CAD algorithms, use of CAD for automatic reading or prescreening may become feasible. This work addresses the question how to fuse information from multiple CAD algorithms, operating on multiple images that comprise an exam, to determine a likelihood that the exam is normal and would not require further inspection by human operators. We focus on retinal image screening for diabetic retinopathy, a common complication of diabetes. Current CAD systems are not designed to automatically evaluate complete exams consisting of multiple images for which several detection algorithm output sets are available. Information fusion will potentially play a crucial role in enabling the application of CAD technology to the automatic screening problem. Several different fusion methods are proposed and their effect on the performance of a complete comprehensive automatic diabetic retinopathy screening system is evaluated. Experiments show that the choice of fusion method can have a large impact on system performance. The complete system was evaluated on a set of 15,000 exams (60,000 images). The best performing fusion method obtained an area under the receiver operator characteristic curve of 0.881. This indicates that automated prescreening could be applied in diabetic retinopathy screening programs.
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Affiliation(s)
- Meindert Niemeijer
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242 USA.
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