1
|
Yang L, Zhang M, Cheng J, Zhang T, Lu F. Retina images classification based on 2D empirical mode decomposition and multifractal analysis. Heliyon 2024; 10:e27391. [PMID: 38509989 PMCID: PMC10950613 DOI: 10.1016/j.heliyon.2024.e27391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 02/21/2024] [Accepted: 02/28/2024] [Indexed: 03/22/2024] Open
Abstract
Diabetic retinopathy is an ocular disease caused by long-term damage to the retina due to high blood sugar levels. Elevated blood sugar can impair the microvasculature in the retina, leading to vascular abnormalities and the formation of abnormal new blood vessels. These changes can manifest in the retina as hemorrhages, leaks, vessel dilation, retinal edema, and retinal detachment. The retinas of individuals with diabetes exhibit different morphologies compared to those without the condition. Most histological images cannot be accurately described using traditional geometric shapes or methods. Therefore, this study aims to evaluate and classify the morphology of retinas with varying degrees of severity using multifractal geometry. In the initial experiments, two-dimensional empirical mode decomposition was employed to extract high-frequency detailed features, and the classification process was based on the most relevant features in the multifractal spectrum associated with disease factors. To eliminate less significant features, the random forest algorithm was utilized. The proposed method achieved an accuracy of 96%, sensitivity of 96%, and specificity of 95%.
Collapse
Affiliation(s)
- Lei Yang
- School of Mechatronic Engineering and Automation, Shanghai University, China
| | - Minxuan Zhang
- School of Mechatronic Engineering and Automation, Shanghai University, China
| | - Jing Cheng
- College of Electrical Engineering, Sichuan University, China
| | - Tiegang Zhang
- School of Mechatronic Engineering and Automation, Shanghai University, China
| | - Feng Lu
- School of Mechatronic Engineering and Automation, Shanghai University, China
| |
Collapse
|
2
|
Khandouzi A, Ariafar A, Mashayekhpour Z, Pazira M, Baleghi Y. Retinal Vessel Segmentation, a Review of Classic and Deep Methods. Ann Biomed Eng 2022; 50:1292-1314. [PMID: 36008569 DOI: 10.1007/s10439-022-03058-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 08/15/2022] [Indexed: 11/01/2022]
Abstract
Retinal illnesses such as diabetic retinopathy (DR) are the main causes of vision loss. In the early recognition of eye diseases, the segmentation of blood vessels in retina images plays an important role. Different symptoms of ocular diseases can be identified by the geometric features of ocular arteries. However, due to the complex construction of the blood vessels and their different thicknesses, segmenting the retina image is a challenging task. There are a number of algorithms that helped the detection of retinal diseases. This paper presents an overview of papers from 2016 to 2022 that discuss machine learning and deep learning methods for automatic vessel segmentation. The methods are divided into two groups: Deep learning-based, and classic methods. Algorithms, classifiers, pre-processing and specific techniques of each group is described, comprehensively. The performances of recent works are compared based on their achieved accuracy in different datasets in inclusive tables. A survey of most popular datasets like DRIVE, STARE, HRF and CHASE_DB1 is also given in this paper. Finally, a list of findings from this review is presented in the conclusion section.
Collapse
Affiliation(s)
- Ali Khandouzi
- Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Ali Ariafar
- Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Zahra Mashayekhpour
- Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Milad Pazira
- Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Yasser Baleghi
- Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.
| |
Collapse
|
3
|
Yue T, Yang W, Liao Q. CCNET: Cross Coordinate Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2062-2065. [PMID: 36085646 DOI: 10.1109/embc48229.2022.9871284] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
With the rapid development of the world economy and increasing improvement of people's living standards, the number of diabetic patients has been growing quickly. Meanwhile, the complications of diabetes especially retinopathy have been affecting their daily life seriously. The only way to prevent it from getting worse and even leading to blindness is to make corresponding diagnosis as early as possible. However, it's extremely impossible for professionals to diagnose all the patients through their fundus images. It couldn't be better to solve the problem by automatic systems, so we present a novel network to learn the features of diabetic retinopathy (DR) and its complication diabetic macular edema (DME) and the relationship between them, focus on some vital areas in the pictures and eventually obtain the grades of the two diseases at the same time. Experimental results further prove the effectiveness of our proposed module comparing to the only joint grading network before.
Collapse
|
4
|
Guo F, Li W, Shen Z, Shi X. MTCLF: A multitask curriculum learning framework for unbiased glaucoma screenings. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106910. [PMID: 35660942 DOI: 10.1016/j.cmpb.2022.106910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 05/12/2022] [Accepted: 05/24/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Glaucoma is a disease that causes irreversible damage to the optic nerve. Research on accurate automatic screening algorithms is essential for the prevention and treatment of glaucoma. However, due to the imbalance of existing datasets and the existence of some hard samples that accompany other diverse and complex fundus diseases, the performance of current glaucoma screening algorithms is limited. In addition, the lack of interpretability also makes it difficult for the current algorithms to meet the requirements of clinical applications. METHOD In this paper, we propose a new multitask curriculum learning framework (MTCLF) for unbiased glaucoma screenings and visualizations of model decision-making areas. MTCLF is a teacher-student framework. The teacher network is used to generate the label evidence map. The student network can diagnose glaucoma and predict the evidence map at the same time with the well-designed dual-branch CNN structure and collaborative learning module. We design two curriculum coefficients θ and σ to guide the training process of the student network in the sample space so that the student network can adaptively balance the sample contribution, reduce the prediction bias and mine hard samples. RESULTS The experimental results show that the accuracy, sensitivity, specificity, AUC and F2-score of MTCLF based on the LAG dataset for glaucoma diagnoses are 0.967, 0.961, 0.970, 0.996, and 0.958, respectively. These results are superior to those of the state-of-the-art methods. CONCLUSION MTCLF not only achieves the best performance for unbiased glaucoma diagnoses but also generates a reliable evidence map to help clinicians explore fine lesion areas.
Collapse
Affiliation(s)
- Fan Guo
- School of Automation, Central South University, Changsha 410083, China.
| | - Weiqing Li
- School of Automation, Central South University, Changsha 410083, China
| | - Ziqi Shen
- School of Automation, Central South University, Changsha 410083, China
| | - Xiangyu Shi
- School of Automation, Central South University, Changsha 410083, China
| |
Collapse
|
5
|
Improved Security of E-Healthcare Images Using Hybridized Robust Zero-Watermarking and Hyper-Chaotic System along with RSA. MATHEMATICS 2022. [DOI: 10.3390/math10071071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
With the rapid advancements of the internet of things (IoT), several applications have evolved with completely dissimilar structures and requirements. However, the fifth generation of mobile cellular networks (5G) is unable to successfully support the dissimilar structures and requirements. The sixth generation of mobile cellular networks (6G) is likely to enable new and unidentified applications with varying requirements. Therefore, 6G not only provides 10 to 100 times the speed of 5G, but 6G can also provide dynamic services for advanced IoT applications. However, providing security to 6G networks is still a significant problem. Therefore, in this paper, a hybrid image encryption technique is proposed to secure multimedia data communication over 6G networks. Initially, multimedia data are encrypted by using the proposed model. Thereafter, the encrypted data are then transferred over the 6G networks. Extensive experiments are conducted by using various attacks and security measures. A comparative analysis reveals that the proposed model achieves remarkably good performance as compared to the existing encryption techniques.
Collapse
|
6
|
An Image Examination System for Retinal Optic Disc mining and Analysis with Social Group Optimization Algorithm. INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022. [DOI: 10.4018/ijsir.300370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This work aims to develop a hybrid image examination system to extract and evaluate the Optic Disc (OD) from the Age-related Macular Degeneration (AMD) and Non-AMD class Digital Fundus Retinal Image (DFRI). This work implements an image pre-processing through Shannon’s Entropy and Social Group Optimization (SE+SGO) based thresholding and image post-processing with Level Set Segmentation (LSS). A relative study among the extracted OD and the ground-truth is then executed to compute the vital Picture Similarity Parameters (PSP). This study also presents a detailed pixel level data analysis practice on the extracted OD. Finally, the performance of the LSS is then validated against the existing segmentation techniques, such as Chan-Vese, Active-Contour and k-means clustering. The proposed work is executed on the iChallenge-AMD-2018 DFRI (400 images) and the results confirm that, proposed hybrid tool helps to achieve better values of Jaccard (86.82%), Dice (91.78%), Accuracy (98.94%), Precision (92.86%), Sensitivity (94.06%), and Specificity (99.46%).
Collapse
|
7
|
P. S, J. R, R. P. An automatic recognition of glaucoma in fundus images using deep learning and random forest classifier. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107512] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
|
8
|
Accurate Diagnosis of Diabetic Retinopathy and Glaucoma Using Retinal Fundus Images Based on Hybrid Features and Genetic Algorithm. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11136178] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Diabetic retinopathy (DR) and glaucoma can both be incurable if they are not detected early enough. Therefore, ophthalmologists worldwide are striving to detect them by personally screening retinal fundus images. However, this procedure is not only tedious, subjective, and labor-intensive, but also error-prone. Worse yet, it may not even be attainable in some countries where ophthalmologists are in short supply. A practical solution to this complicated problem is a computer-aided diagnosis (CAD) system—the objective of this work. We propose an accurate system to detect at once any of the two diseases from retinal fundus images. The accuracy stems from two factors. First, we calculate a large set of hybrid features belonging to three groups: first-order statistics (FOS), higher-order statistics (HOS), and histogram of oriented gradient (HOG). Then, these features are skillfully reduced using a genetic algorithm scheme that selects only the most relevant and significant of them. Finally, the selected features are fed to a classifier to detect one of three classes: DR, glaucoma, or normal. Four classifiers are tested for this job: decision tree (DT), naive Bayes (NB), k-nearest neighbor (kNN), and linear discriminant analysis (LDA). The experimental work, conducted on three publicly available datasets, two of them merged into one, shows impressive performance in terms of four standard classification metrics, each computed using k-fold crossvalidation for added credibility. The highest accuracy has been provided by DT—96.67% for DR, 100% for glaucoma, and 96.67% for normal.
Collapse
|
9
|
Automated segmentation of optic disc and optic cup for glaucoma assessment using improved UNET++ architecture. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.05.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
|
10
|
Xu J, Yang W, Wan C, Shen J. Weakly supervised detection of central serous chorioretinopathy based on local binary patterns and discrete wavelet transform. Comput Biol Med 2020; 127:104056. [PMID: 33096297 DOI: 10.1016/j.compbiomed.2020.104056] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/10/2020] [Accepted: 10/10/2020] [Indexed: 10/23/2022]
Abstract
Central serous chorioretinopathy (CSCR) is a common fundus disease. Early detection of CSCR is of great importance to prevent visual loss. Therefore, a novel automatic detection method is presented in this paper which integrates technologies including discrete wavelet transform (DWT) image decomposition, local binary patterns (LBP) based texture feature extraction, and multi-instance learning (MIL). LBP is selected due to its robustness to low contrast and low quality images, which can reduce the interference of image itself on the detection method. DWT image decomposition provides high-frequency components with rich details for extracting LBP texture features, which can remove redundant information that is not necessary for diagnosis of CSCR in the raw image. The tedious task of accurately locating and segmenting CSCR lesions is avoided by using MIL. Experiments on 358 optical coherence tomography (OCT) B-scan images demonstrate the effectiveness of our method. Even under the condition of single threshold, the accuracy of 99.58% is obtained at K = 35 by only using a high-frequency feature fusion scheme, which is competitive with the existing methods. Additionally, through further detail innovation, such as multi-threshold optimization (MTO) and integrated decision-making (IDM), the performance of our method is further improved and the detection accuracy is 100% at K = 40.
Collapse
Affiliation(s)
- Jianguo Xu
- College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics &Astronautics, 210016, Nanjing, PR China.
| | - Weihua Yang
- The Affiliated Eye Hospital of Nanjing Medical University, 210029, Nanjing, PR China
| | - Cheng Wan
- College of Electronic and Information Engineering, Nanjing University of Aeronautics & Astronautics, 211106, Nanjing, PR China
| | - Jianxin Shen
- College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics &Astronautics, 210016, Nanjing, PR China.
| |
Collapse
|
11
|
Bibi I, Mir J, Raja G. Automated detection of diabetic retinopathy in fundus images using fused features. Phys Eng Sci Med 2020; 43:1253-1264. [PMID: 32955686 DOI: 10.1007/s13246-020-00929-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 09/14/2020] [Indexed: 10/23/2022]
Abstract
Diabetic retinopathy (DR) is one of the severe eye conditions due to diabetes complication which can lead to vision loss if left untreated. In this paper, a computationally simple, yet very effective, DR detection method is proposed. First, a segmentation independent two-stage preprocessing based technique is proposed which can effectively extract DR pathognomonic signs; both bright and red lesions, and blood vessels from the eye fundus image. Then, the performance of Local Binary Patterns (LBP), Local Ternary Patterns (LTP), Dense Scale-Invariant Feature Transform (DSIFT) and Histogram of Oriented Gradients (HOG) as a feature descriptor for fundus images, is thoroughly analyzed. SVM kernel-based classifiers are trained and tested, using a 5-fold cross-validation scheme, on both newly acquired fundus image database from the local hospital and combined database created from the open-sourced available databases. The classification accuracy of 96.6% with 0.964 sensitivity and 0.969 specificity is achieved using a Cubic SVM classifier with LBP and LTP fused features for the local database. More importantly, in out-of-sample testing on the combined database, the model gives an accuracy of 95.21% with a sensitivity of 0.970 and specificity of 0.932. This indicates the proposed model is very well-fitted and generalized which is further corroborated by the presented train-test curves.
Collapse
Affiliation(s)
- Iqra Bibi
- Electrical Engineering Department, University of Engineering and Technology, Taxila, Pakistan
| | - Junaid Mir
- Electrical Engineering Department, University of Engineering and Technology, Taxila, Pakistan
| | - Gulistan Raja
- Electrical Engineering Department, University of Engineering and Technology, Taxila, Pakistan.
| |
Collapse
|
12
|
Classification of glaucoma using hybrid features with machine learning approaches. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102137] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
13
|
Automated glaucoma screening method based on image segmentation and feature extraction. Med Biol Eng Comput 2020; 58:2567-2586. [PMID: 32820355 DOI: 10.1007/s11517-020-02237-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 07/25/2020] [Indexed: 12/16/2022]
Abstract
Glaucoma is a chronic disease that threatens eye health and can cause permanent blindness. Since there is no cure for glaucoma, early screening and detection are crucial for the prevention of glaucoma. Therefore, a novel method for automatic glaucoma screening that combines clinical measurement features with image-based features is proposed in this paper. To accurately extract clinical measurement features, an improved UNet++ neural network is proposed to segment the optic disc and optic cup based on region of interest (ROI) simultaneously. Some important clinical measurement features, such as optic cup to disc ratio, are extracted from the segmentation results. Then, the increasing field of view (IFOV) feature model is proposed to fully extract texture features, statistical features, and other hidden image-based features. Next, we select the best feature combination from all the features and use the adaptive synthetic sampling approach to alleviate the uneven distribution of training data. Finally, a gradient boosting decision tree (GBDT) classifier for glaucoma screening is trained. Experimental results based on the ORIGA dataset show that the proposed algorithm achieves excellent glaucoma screening performance with sensitivity of 0.894, accuracy of 0.843, and AUC of 0.901, which is superior to other existing methods.Graphical abstract Framework of the proposed glaucoma classification method.
Collapse
|
14
|
Wang J, Shao W, Kim J. Combining MF-DFA and LSSVM for retina images classification. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101943] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
15
|
Sarhan A, Rokne J, Alhajj R. Glaucoma detection using image processing techniques: A literature review. Comput Med Imaging Graph 2019; 78:101657. [PMID: 31675645 DOI: 10.1016/j.compmedimag.2019.101657] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 09/02/2019] [Accepted: 09/09/2019] [Indexed: 11/26/2022]
Abstract
The term glaucoma refers to a group of heterogeneous diseases that cause the degeneration of retinal ganglion cells (RGCs). The degeneration of RGCs leads to two main issues: (i) structural changes to the optic nerve head as well as the nerve fiber layer, and (ii) simultaneous functional failure of the visual field. These two effects of glaucoma may lead to peripheral vision loss and, if the condition is left to progress it may eventually lead to blindness. No cure for glaucoma exists apart from early detection and treatment by optometrists and ophthalmologists. The degeneration of RGCs is normally detected from retinal images which are assessed by an expert. These retinal images also provide other vital information about the health of an eye. Thus, it is essential to develop automated techniques for extracting this information. The rapid development of digital images and computer vision techniques have increased the potential for analysis of eye health from images. This paper surveys current approaches to detect glaucoma from 2D and 3D images; both the limitations and possible future directions are highlighted. This study also describes the datasets used for retinal analysis along with existing evaluation algorithms. The main topics covered by this study may be enumerated as follows.
Collapse
Affiliation(s)
- Abdullah Sarhan
- Department of Computer Science, University of Calgary, Calgary, AB, Canada.
| | - Jon Rokne
- Department of Computer Science, University of Calgary, Calgary, AB, Canada
| | - Reda Alhajj
- Department of Computer Science, University of Calgary, Calgary, AB, Canada; Department of Computer Engineering, Istanbul Medipol University, Istanbul, Turkey
| |
Collapse
|
16
|
Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy. Symmetry (Basel) 2019. [DOI: 10.3390/sym11060749] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.
Collapse
|
17
|
Jiménez-García J, Romero-Oraá R, García M, López-Gálvez MI, Hornero R. Combination of Global Features for the Automatic Quality Assessment of Retinal Images. ENTROPY 2019; 21:e21030311. [PMID: 33267025 PMCID: PMC7514792 DOI: 10.3390/e21030311] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 03/14/2019] [Accepted: 03/18/2019] [Indexed: 02/02/2023]
Abstract
Diabetic retinopathy (DR) is one of the most common causes of visual loss in developed countries. Computer-aided diagnosis systems aimed at detecting DR can reduce the workload of ophthalmologists in screening programs. Nevertheless, a large number of retinal images cannot be analyzed by physicians and automatic methods due to poor quality. Automatic retinal image quality assessment (RIQA) is needed before image analysis. The purpose of this study was to combine novel generic quality features to develop a RIQA method. Several features were calculated from retinal images to achieve this goal. Features derived from the spatial and spectral entropy-based quality (SSEQ) and the natural images quality evaluator (NIQE) methods were extracted. They were combined with novel sharpness and luminosity measures based on the continuous wavelet transform (CWT) and the hue saturation value (HSV) color model, respectively. A subset of non-redundant features was selected using the fast correlation-based filter (FCBF) method. Subsequently, a multilayer perceptron (MLP) neural network was used to obtain the quality of images from the selected features. Classification results achieved 91.46% accuracy, 92.04% sensitivity, and 87.92% specificity. Results suggest that the proposed RIQA method could be applied in a more general computer-aided diagnosis system aimed at detecting a variety of retinal pathologies such as DR and age-related macular degeneration.
Collapse
Affiliation(s)
- Jorge Jiménez-García
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
- Correspondence: ; Tel.: +34-983-18-47-16
| | - Roberto Romero-Oraá
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - María García
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - María I. López-Gálvez
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
- Department of Ophthalmology, Hospital Clínico Universitario de Valladolid, Avenida Ramón y Cajal 3, 47003 Valladolid, Spain
- Instituto de Oftalmobiología Aplicada, University of Valladolid, Paseo de Belén 17, 47011 Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
- Instituto de Investigación en Matemáticas (IMUVA), University of Valladolid, 47011 Valladolid, Spain
- Instituto de Neurociencias de Castilla y León (INCYL), University of Salamanca, 37007 Salamanca, Spain
| |
Collapse
|
18
|
Wang Y, Soetikno B, Furst J, Raicu D, Fawzi AA. Drusen diagnosis comparison between hyper-spectral and color retinal images. BIOMEDICAL OPTICS EXPRESS 2019; 10:914-931. [PMID: 30800523 PMCID: PMC6377880 DOI: 10.1364/boe.10.000914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 01/04/2019] [Accepted: 01/07/2019] [Indexed: 06/09/2023]
Abstract
Age-related macular degeneration (AMD) is a degenerative aging disorder, which can lead to irreversible vision loss in older individuals. The emergence of clinical applications of retinal hyper-spectral imaging provides a unique opportunity to capture important spectral signatures, with the potential to enhance the molecular diagnosis of retinal diseases. In this study, we use a machine learning classification approach to explore whether hyper-spectral images offer an improved outcome compared to standard RGB images. Our results show that the classifier performs better on hyper-spectral images with improved accuracy and sensitivity for drusen classification compared to standard imaging. By examining the most important features in the classification task, our data suggest that drusen are highly heterogeneous. Our work provides further evidence that hyper-spectral retinal image data are uniquely suited for computer-aided diagnosis and detection techniques.
Collapse
Affiliation(s)
- Yiyang Wang
- College of Computing and Digital Media, DePaul University, Chicago, Illinois, 60604, USA
| | - Brian Soetikno
- Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Functional Optical Imaging Laboratory, Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Jacob Furst
- College of Computing and Digital Media, DePaul University, Chicago, Illinois, 60604, USA
| | - Daniela Raicu
- College of Computing and Digital Media, DePaul University, Chicago, Illinois, 60604, USA
| | - Amani A Fawzi
- Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| |
Collapse
|
19
|
Hagiwara Y, Koh JEW, Tan JH, Bhandary SV, Laude A, Ciaccio EJ, Tong L, Acharya UR. Computer-aided diagnosis of glaucoma using fundus images: A review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 165:1-12. [PMID: 30337064 DOI: 10.1016/j.cmpb.2018.07.012] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 07/02/2018] [Accepted: 07/25/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVES Glaucoma is an eye condition which leads to permanent blindness when the disease progresses to an advanced stage. It occurs due to inappropriate intraocular pressure within the eye, resulting in damage to the optic nerve. Glaucoma does not exhibit any symptoms in its nascent stage and thus, it is important to diagnose early to prevent blindness. Fundus photography is widely used by ophthalmologists to assist in diagnosis of glaucoma and is cost-effective. METHODS The morphological features of the disc that is characteristic of glaucoma are clearly seen in the fundus images. However, manual inspection of the acquired fundus images may be prone to inter-observer variation. Therefore, a computer-aided detection (CAD) system is proposed to make an accurate, reliable and fast diagnosis of glaucoma based on the optic nerve features of fundus imaging. In this paper, we reviewed existing techniques to automatically diagnose glaucoma. RESULTS The use of CAD is very effective in the diagnosis of glaucoma and can assist the clinicians to alleviate their workload significantly. We have also discussed the advantages of employing state-of-art techniques, including deep learning (DL), when developing the automated system. The DL methods are effective in glaucoma diagnosis. CONCLUSIONS Novel DL algorithms with big data availability are required to develop a reliable CAD system. Such techniques can be employed to diagnose other eye diseases accurately.
Collapse
Affiliation(s)
- Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Joel En Wei Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Jen Hong Tan
- National University of Singapore, Institute of System Science
| | | | - Augustinus Laude
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | | | - Louis Tong
- Ocular Surface Research Group, Singapore Eye Research Institute, Singapore; Cornea and External Eye Disease Service, Singapore National Eye Center, Singapore; Eye Academic Clinical Program, Duke-NUS Medical School, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore School of Social Sciences, Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, Malaysia.
| |
Collapse
|
20
|
Abraham B, Nair MS. Computer-aided diagnosis of clinically significant prostate cancer from MRI images using sparse autoencoder and random forest classifier. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.06.009] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
|
21
|
Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017. INFORMATICS IN MEDICINE UNLOCKED 2018. [DOI: 10.1016/j.imu.2018.03.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
|
22
|
Koh JEW, Ng EYK, Bhandary SV, Hagiwara Y, Laude A, Acharya UR. Automated retinal health diagnosis using pyramid histogram of visual words and Fisher vector techniques. Comput Biol Med 2017; 92:204-209. [PMID: 29227822 DOI: 10.1016/j.compbiomed.2017.11.019] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 11/27/2017] [Accepted: 11/30/2017] [Indexed: 12/18/2022]
Abstract
Untreated age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma may lead to irreversible vision loss. Hence, it is essential to have regular eye screening to detect these eye diseases at an early stage and to offer treatment where appropriate. One of the simplest, non-invasive and cost-effective techniques to screen the eyes is by using fundus photo imaging. But, the manual evaluation of fundus images is tedious and challenging. Further, the diagnosis made by ophthalmologists may be subjective. Therefore, an objective and novel algorithm using the pyramid histogram of visual words (PHOW) and Fisher vectors is proposed for the classification of fundus images into their respective eye conditions (normal, AMD, DR, and glaucoma). The proposed algorithm extracts features which are represented as words. These features are built and encoded into a Fisher vector for classification using random forest classifier. This proposed algorithm is validated with both blindfold and ten-fold cross-validation techniques. An accuracy of 90.06% is achieved with the blindfold method, and highest accuracy of 96.79% is obtained with ten-fold cross-validation. The highest classification performance of our system shows the potential of deploying it in polyclinics to assist healthcare professionals in their initial diagnosis of the eye. Our developed system can reduce the workload of ophthalmologists significantly.
Collapse
Affiliation(s)
- Joel E W Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.
| | - Eddie Y K Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
| | | | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Augustinus Laude
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia
| |
Collapse
|
23
|
Koh JEW, Ng EYK, Bhandary SV, Laude A, Acharya UR. Automated detection of retinal health using PHOG and SURF features extracted from fundus images. APPL INTELL 2017. [DOI: 10.1007/s10489-017-1048-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|