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Shahriari MH, Sabbaghi H, Asadi F, Hosseini A, Khorrami Z. Artificial intelligence in screening, diagnosis, and classification of diabetic macular edema: A systematic review. Surv Ophthalmol 2023; 68:42-53. [PMID: 35970233 DOI: 10.1016/j.survophthal.2022.08.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 08/03/2022] [Accepted: 08/08/2022] [Indexed: 02/01/2023]
Abstract
We review the application of artificial intelligence (AI) techniques in the screening, diagnosis, and classification of diabetic macular edema (DME) by searching six databases- PubMed, Scopus, Web of Science, Science Direct, IEEE, and ACM- from January 1, 2005 to July 4, 2021. A total of 879 articles were extracted, and by applying inclusion and exclusion criteria, 38 articles were selected for more evaluation. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). We provide an overview of the current state of various AI techniques for DME screening, diagnosis, and classification using retinal imaging modalities such as optical coherence tomography (OCT) and color fundus photography (CFP). Based on our findings, deep learning models have an extraordinary capacity to provide an accurate and efficient system for DME screening and diagnosis. Using these in the processing of modalities leads to a significant increase in sensitivity and specificity values. The use of decision support systems and applications based on AI in processing retinal images provided by OCT and CFP increases the sensitivity and specificity in DME screening and detection.
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Affiliation(s)
- Mohammad Hasan Shahriari
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamideh Sabbaghi
- Ophthalmic Epidemiology Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Optometry, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Azamosadat Hosseini
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Zahra Khorrami
- Ophthalmic Epidemiology Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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2
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Pavithra K, Kumar P, Geetha M, Bhandary SV. Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review. Biocybern Biomed Eng 2023. [DOI: 10.1016/j.bbe.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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3
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A Novel original feature fusion network for joint diabetic retinopathy and diabetic Macular edema grading. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-08038-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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4
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Kumar A, Tewari AS. Risk Identification of Diabetic Macular Edema Using E-Adoption of Emerging Technology. INTERNATIONAL JOURNAL OF E-ADOPTION 2022. [DOI: 10.4018/ijea.310000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The accumulation of the blood leaks on the retina is known as diabetic macular edema (DME), which can result in irreversible blindness. Early diagnosis and therapy can stop DME. This study presents an e-adoption of emerging technology such as RadioDense model for detecting and classifying DME from retinal fundus images. The proposed model employs a modified version of DenseNet121, radiomics features, and the gradient boosting classifier. The authors evaluated many classifiers on the concatenated features. The efficacy of the classifier is determined by comparing each classifier's accuracy values. According to the evaluation results, the concatenated features extraction using gradient boosting classifier outperforms all other classifiers on the IDRiD dataset. For multi-class classification, the suggested electronic adoption of emerging technology such as RadioDense model outperformed these classifiers and attained an accuracy of 87.4%. It can help to decrease the strain of ophthalmologists diagnosing the DME during locking and unlocking the worldwide lockdown.
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Affiliation(s)
- Amit Kumar
- National Institute of Technology, Patna, India
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5
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Biswas S, Khan MIA, Hossain MT, Biswas A, Nakai T, Rohdin J. Which Color Channel Is Better for Diagnosing Retinal Diseases Automatically in Color Fundus Photographs? LIFE (BASEL, SWITZERLAND) 2022; 12:life12070973. [PMID: 35888063 PMCID: PMC9321111 DOI: 10.3390/life12070973] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 05/25/2022] [Accepted: 06/01/2022] [Indexed: 11/22/2022]
Abstract
Color fundus photographs are the most common type of image used for automatic diagnosis of retinal diseases and abnormalities. As all color photographs, these images contain information about three primary colors, i.e., red, green, and blue, in three separate color channels. This work aims to understand the impact of each channel in the automatic diagnosis of retinal diseases and abnormalities. To this end, the existing works are surveyed extensively to explore which color channel is used most commonly for automatically detecting four leading causes of blindness and one retinal abnormality along with segmenting three retinal landmarks. From this survey, it is clear that all channels together are typically used for neural network-based systems, whereas for non-neural network-based systems, the green channel is most commonly used. However, from the previous works, no conclusion can be drawn regarding the importance of the different channels. Therefore, systematic experiments are conducted to analyse this. A well-known U-shaped deep neural network (U-Net) is used to investigate which color channel is best for segmenting one retinal abnormality and three retinal landmarks.
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Affiliation(s)
- Sangeeta Biswas
- Faculty of Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.I.A.K.); (M.T.H.)
- Correspondence: or
| | - Md. Iqbal Aziz Khan
- Faculty of Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.I.A.K.); (M.T.H.)
| | - Md. Tanvir Hossain
- Faculty of Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh; (M.I.A.K.); (M.T.H.)
| | - Angkan Biswas
- CAPM Company Limited, Bonani, Dhaka 1213, Bangladesh;
| | - Takayoshi Nakai
- Faculty of Engineering, Shizuoka University, Hamamatsu 432-8561, Japan;
| | - Johan Rohdin
- Faculty of Information Technology, Brno University of Technology, 61200 Brno, Czech Republic;
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Chandrasekaran R, Loganathan B. Retinopathy grading with deep learning and wavelet hyper-analytic activations. THE VISUAL COMPUTER 2022; 39:1-16. [PMID: 35493724 PMCID: PMC9035984 DOI: 10.1007/s00371-022-02489-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 03/12/2022] [Indexed: 06/14/2023]
Abstract
Recent developments reveal the prominence of Diabetic Retinopathy (DR) grading. In the past few decades, Wavelet-based DR classification has shown successful impacts and the Deep Learning models, like Convolutional Neural Networks (CNN's), have evolved in offering the highest prediction accuracy. In this work, the features of the input image are enhanced with the integration of Multi-Resolution Analysis (MRA) and a CNN framework without costing more convolution filters. The bottleneck with conventional activation functions, used in CNN's, is the nullification of the feature maps that are negative in value. In this work, a novel Hyper-analytic Wavelet (HW) phase activation function is formulated with unique characteristics for the wavelet sub-bands. Instead of dismissal, the function transforms these negative coefficients that correspond to significant edge feature maps. The hyper-analytic wavelet phase forms the imaginary part of the complex activation. And the hyper-parameter of the activation function is selected such that the corresponding magnitude spectrum produces monotonic and effective activations. The performance of 3 CNN models (1 custom, shallow CNN, ResNet with Soft attention, Alex Net for DR) with spatial-Wavelet quilts is better. With the spatial-Wavelet quilts, the Alex Net for DR has an improvement with an 11% of accuracy level (from 87 to 98%). The highest accuracy level of 98% and the highest Sensitivity of 99% are attained through Modified Alex Net for DR. The proposal also illustrates the visualization of the negative edge preservation with assumed image patches. From this study, the researcher infers that models with spatial-Wavelet quilts, with the hyper-analytic activations, have better generalization ability. And the visualization of heat maps provides evidence of better learning of the feature maps from the wavelet sub-bands.
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Affiliation(s)
| | - Balaji Loganathan
- Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, Tamil Nadu 600062 India
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Hei Y, Yuan T, Fan Z, Yang B, Hu J. Sleep staging classification based on a new parallel fusion method of multiple sources signals. Physiol Meas 2022; 43. [PMID: 35381584 DOI: 10.1088/1361-6579/ac647b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 04/05/2022] [Indexed: 11/12/2022]
Abstract
APPROACH First, the heart rate variability (HRV) is extracted from EOG with the Weight Calculation Algorithm (WCA) and an "HYF" RR interval detection algorithm. Second, three feature sets were extracted from HRV segments and EOG segments: time-domain features, frequency domain features and nonlinear-domain features. The frequency domain features and nonlinear-domain features were extracted by using Discrete Wavelet Transform (DWT), Autoregressive (AR), and Power Spectral entropy (PSE), and Refined Composite Multiscale Dispersion Entropy (RCMDE). Third, a new "Parallel Fusion Method" (PFM) for sleep stage classification is proposed. Three kinds of feature sets from EOG and HRV segments are fused by using PFM. Fourth, Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) classification models is employed for sleep staging. MAIN RESULTS Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the new sleep staging approach. The performance of the proposed method is testedby evaluating the average accuracy, Kappa coefficient. The average accuracy of sleep classification results by using XGBoost classification model with PFM is 82.7% and the kappa coefficient is 0.711, also by using SVM classification model with the PFM is 83.7%, and the kappa coefficient is 0.724. Experimental results show that the performance of the proposed method is competitive with the most current methods and results, and the recognition rate of S1 stage is significantly improved. Significance: As a consequence, it would enable one to improve the quality of automatic sleep staging models when the EOG and HRV signals are fused, which can be beneficial for monitor sleep quality and keep abreast of health conditions. Besides, our study provides good research ideas and methods for scholars, doctors and individuals.
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Affiliation(s)
- Yafang Hei
- College of Applied Mathematics, Chengdu University of Information Technology, Xuefu road 24, Shuangliu, Chengdu, Chengdu, Sichuan, 610225, CHINA
| | - Tuming Yuan
- College of Applied Mathematics, Chengdu University of Information Technology, Xuefu road 24, Chengdu, Sichuan, 610225, CHINA
| | - Zhigao Fan
- School of Atmospheric Sciences, Chengdu University of Information Technology, Xuefu road 24, Chengdu, Sichuan, 610225, CHINA
| | - Bo Yang
- College of Electronic Engineering, Chengdu University of Information Technology, Xuefu road 24, , , 610225, CHINA
| | - Jiancheng Hu
- College of Applied Mathematics, Chengdu University of Information Technology, Xuefu road 24, , , 610225, CHINA
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8
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Gour N, Tanveer M, Khanna P. Challenges for ocular disease identification in the era of artificial intelligence. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06770-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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9
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Classification of diabetic retinopathy using unlabeled data and knowledge distillation. Artif Intell Med 2021; 121:102176. [PMID: 34763798 DOI: 10.1016/j.artmed.2021.102176] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 09/11/2021] [Accepted: 09/13/2021] [Indexed: 11/22/2022]
Abstract
Over the last decade, advances in Machine Learning and Artificial Intelligence have highlighted their potential as a diagnostic tool in the healthcare domain. Despite the widespread availability of medical images, their usefulness is severely hampered by a lack of access to labeled data. For example, while Convolutional Neural Networks (CNNs) have emerged as an essential analytical tool in image processing, their impact is curtailed by training limitations due to insufficient labeled data availability. Transfer Learning enables models developed for one task to be reused for a second task. Knowledge distillation enables transferring knowledge from a pre-trained model to another. However, it suffers from limitations, and the two models' constraints need to be architecturally similar. Knowledge distillation addresses some of the shortcomings of transfer learning by generalizing a complex model to a lighter model. However, some parts of the knowledge may not be distilled by knowledge distillation sufficiently. In this paper, a novel knowledge distillation approach using transfer learning is proposed. The proposed approach transfers the complete knowledge of a model to a new smaller one. Unlabeled data are used in an unsupervised manner to transfer the new smaller model's maximum amount of knowledge. The proposed method can be beneficial in medical image analysis, where labeled data are typically scarce. The proposed approach is evaluated in classifying images for diagnosing Diabetic Retinopathy on two publicly available datasets, including Messidor and EyePACS. Simulation results demonstrate that the approach effectively transfers knowledge from a complex model to a lighter one. Furthermore, experimental results illustrate that different small models' performance is improved significantly using unlabeled data and knowledge distillation.
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Region of interest-based predictive algorithm for subretinal hemorrhage detection using faster R-CNN. Soft comput 2021; 25:15255-15268. [PMID: 34421341 PMCID: PMC8371433 DOI: 10.1007/s00500-021-06098-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/30/2021] [Indexed: 11/04/2022]
Abstract
Macular edema (ME) is an essential sort of macular issue caused due to the storing of fluid underneath the macula. Age-related Macular Degeneration (AMD) and diabetic macular edema (DME) are the two customary visual contaminations that can lead to fragmentary or complete vision loss. This paper proposes a deep learning-based predictive algorithm that can be used to detect the presence of a Subretinal hemorrhage. Region Convolutional Neural Network (R-CNN) and faster R-CNN are used to develop the predictive algorithm that can improve the classification accuracy. This method initially detects the presence of Subretinal hemorrhage, and it then segments the Region of Interest (ROI) by a semantic segmentation process. The segmented ROI is applied to a predictive algorithm which is derived from the Fast Region Convolutional Neural Network algorithm, that can categorize the Subretinal hemorrhage as responsive or non-responsive. The dataset, provided by a medical institution, comprised of optical coherence tomography (OCT) images of both pre- and post-treatment images, was used for training the proposed Faster Region Convolutional Neural Network (Faster R-CNN). We also used the Kaggle dataset for performance comparison with the traditional methods that are derived from the convolutional neural network (CNN) algorithm. The evaluation results using the Kaggle dataset and the hospital images provide an average sensitivity, selectivity, and accuracy of 85.3%, 89.64%, and 93.48% respectively. Further, the proposed method provides a time complexity in testing as 2.64s, which is less than the traditional schemes like CNN, R-CNN, and Fast R-CNN.
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Nazir T, Nawaz M, Rashid J, Mahum R, Masood M, Mehmood A, Ali F, Kim J, Kwon HY, Hussain A. Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model. SENSORS 2021; 21:s21165283. [PMID: 34450729 PMCID: PMC8398326 DOI: 10.3390/s21165283] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 07/22/2021] [Accepted: 07/23/2021] [Indexed: 02/06/2023]
Abstract
Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.
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Affiliation(s)
- Tahira Nazir
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Marriam Nawaz
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Junaid Rashid
- Department of Computer Science and Engineering, Kongju National University, Gongju 31080, Chungcheongnam-do, Korea;
- Correspondence: (J.R.); (H.-Y.K.)
| | - Rabbia Mahum
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Momina Masood
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Awais Mehmood
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Farooq Ali
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Jungeun Kim
- Department of Computer Science and Engineering, Kongju National University, Gongju 31080, Chungcheongnam-do, Korea;
| | - Hyuk-Yoon Kwon
- Research Center for Electrical and Information Technology, Department of Industrial Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
- Correspondence: (J.R.); (H.-Y.K.)
| | - Amir Hussain
- Centre of AI and Data Science, Edinburgh Napier University, Edinburgh EH11 4DY, UK;
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Chalakkal R, Hafiz F, Abdulla W, Swain A. An efficient framework for automated screening of Clinically Significant Macular Edema. Comput Biol Med 2020; 130:104128. [PMID: 33529843 DOI: 10.1016/j.compbiomed.2020.104128] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 11/09/2020] [Accepted: 11/16/2020] [Indexed: 11/20/2022]
Abstract
The present study proposes a new approach to automated screening of Clinically Significant Macular Edema (CSME) and addresses two major challenges associated with such screenings, i.e., exudate segmentation and imbalanced datasets. The proposed approach replaces the conventional exudate segmentation based feature extraction by combining a pre-trained deep neural network with meta-heuristic feature selection. A feature space over-sampling technique is being used to overcome the effects of skewed datasets and the screening is accomplished by a k-NN based classifier. The role of each data-processing step (e.g., class balancing, feature selection) and the effects of limiting the region of interest to fovea on the classification performance are critically analyzed. Finally, the selection and implication of operating points on Receiver Operating Characteristic curve are discussed. The results of this study convincingly demonstrate that by following these fundamental practices of machine learning, a basic k-NN based classifier could effectively accomplish the CSME screening.
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Affiliation(s)
- Renoh Chalakkal
- Department of Electrical & Computer Engineering, The University of Auckland, Auckland, New Zealand; oDocs Eye Care, Dunedin, New Zealand.
| | - Faizal Hafiz
- Department of Electrical & Computer Engineering, The University of Auckland, Auckland, New Zealand; oDocs Eye Care, Dunedin, New Zealand.
| | - Waleed Abdulla
- Department of Electrical & Computer Engineering, The University of Auckland, Auckland, New Zealand.
| | - Akshya Swain
- Department of Electrical & Computer Engineering, The University of Auckland, Auckland, New Zealand.
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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.
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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.
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14
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Stolte S, Fang R. A survey on medical image analysis in diabetic retinopathy. Med Image Anal 2020; 64:101742. [PMID: 32540699 DOI: 10.1016/j.media.2020.101742] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 02/03/2020] [Accepted: 05/28/2020] [Indexed: 01/12/2023]
Abstract
Diabetic Retinopathy (DR) represents a highly-prevalent complication of diabetes in which individuals suffer from damage to the blood vessels in the retina. The disease manifests itself through lesion presence, starting with microaneurysms, at the nonproliferative stage before being characterized by neovascularization in the proliferative stage. Retinal specialists strive to detect DR early so that the disease can be treated before substantial, irreversible vision loss occurs. The level of DR severity indicates the extent of treatment necessary - vision loss may be preventable by effective diabetes management in mild (early) stages, rather than subjecting the patient to invasive laser surgery. Using artificial intelligence (AI), highly accurate and efficient systems can be developed to help assist medical professionals in screening and diagnosing DR earlier and without the full resources that are available in specialty clinics. In particular, deep learning facilitates diagnosis earlier and with higher sensitivity and specificity. Such systems make decisions based on minimally handcrafted features and pave the way for personalized therapies. Thus, this survey provides a comprehensive description of the current technology used in each step of DR diagnosis. First, it begins with an introduction to the disease and the current technologies and resources available in this space. It proceeds to discuss the frameworks that different teams have used to detect and classify DR. Ultimately, we conclude that deep learning systems offer revolutionary potential to DR identification and prevention of vision loss.
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Affiliation(s)
- Skylar Stolte
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1275 Center Drive, Biomedical Sciences Building JG56 P.O. Box 116131 Gainesville, FL 32611-6131, USA.
| | - Ruogu Fang
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1275 Center Drive, Biomedical Sciences Building JG56 P.O. Box 116131 Gainesville, FL 32611-6131, USA.
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15
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Li X, Hu X, Yu L, Zhu L, Fu CW, Heng PA. CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1483-1493. [PMID: 31714219 DOI: 10.1109/tmi.2019.2951844] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Diabetic retinopathy (DR) and diabetic macular edema (DME) are the leading causes of permanent blindness in the working-age population. Automatic grading of DR and DME helps ophthalmologists design tailored treatments to patients, thus is of vital importance in the clinical practice. However, prior works either grade DR or DME, and ignore the correlation between DR and its complication, i.e., DME. Moreover, the location information, e.g., macula and soft hard exhaust annotations, are widely used as a prior for grading. Such annotations are costly to obtain, hence it is desirable to develop automatic grading methods with only image-level supervision. In this article, we present a novel cross-disease attention network (CANet) to jointly grade DR and DME by exploring the internal relationship between the diseases with only image-level supervision. Our key contributions include the disease-specific attention module to selectively learn useful features for individual diseases, and the disease-dependent attention module to further capture the internal relationship between the two diseases. We integrate these two attention modules in a deep network to produce disease-specific and disease-dependent features, and to maximize the overall performance jointly for grading DR and DME. We evaluate our network on two public benchmark datasets, i.e., ISBI 2018 IDRiD challenge dataset and Messidor dataset. Our method achieves the best result on the ISBI 2018 IDRiD challenge dataset and outperforms other methods on the Messidor dataset. Our code is publicly available at https://github.com/xmengli999/CANet.
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16
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Chen Z, Zeng Z, Shen H, Zheng X, Dai P, Ouyang P. DN-GAN: Denoising generative adversarial networks for speckle noise reduction in optical coherence tomography images. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101632] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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17
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Porwal P, Pachade S, Kokare M, Deshmukh G, Son J, Bae W, Liu L, Wang J, Liu X, Gao L, Wu T, Xiao J, Wang F, Yin B, Wang Y, Danala G, He L, Choi YH, Lee YC, Jung SH, Li Z, Sui X, Wu J, Li X, Zhou T, Toth J, Baran A, Kori A, Chennamsetty SS, Safwan M, Alex V, Lyu X, Cheng L, Chu Q, Li P, Ji X, Zhang S, Shen Y, Dai L, Saha O, Sathish R, Melo T, Araújo T, Harangi B, Sheng B, Fang R, Sheet D, Hajdu A, Zheng Y, Mendonça AM, Zhang S, Campilho A, Zheng B, Shen D, Giancardo L, Quellec G, Mériaudeau F. IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. Med Image Anal 2019; 59:101561. [PMID: 31671320 DOI: 10.1016/j.media.2019.101561] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 09/09/2019] [Accepted: 09/16/2019] [Indexed: 02/07/2023]
Abstract
Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on "Diabetic Retinopathy - Segmentation and Grading" was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal sub-challenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.
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Affiliation(s)
- Prasanna Porwal
- Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India; School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA.
| | - Samiksha Pachade
- Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India; School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA
| | - Manesh Kokare
- Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India
| | | | | | | | - Lihong Liu
- Ping An Technology (Shenzhen) Co.,Ltd, China
| | | | - Xinhui Liu
- Ping An Technology (Shenzhen) Co.,Ltd, China
| | | | - TianBo Wu
- Ping An Technology (Shenzhen) Co.,Ltd, China
| | - Jing Xiao
- Ping An Technology (Shenzhen) Co.,Ltd, China
| | | | | | - Yunzhi Wang
- School of Electrical and Computer Engineering, University of Oklahoma, USA
| | - Gopichandh Danala
- School of Electrical and Computer Engineering, University of Oklahoma, USA
| | - Linsheng He
- School of Electrical and Computer Engineering, University of Oklahoma, USA
| | - Yoon Ho Choi
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Yeong Chan Lee
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Zhongyu Li
- Department of Computer Science, University of North Carolina at Charlotte, USA
| | - Xiaodan Sui
- School of Information Science and Engineering, Shandong Normal University, China
| | - Junyan Wu
- Cleerly Inc., New York, United States
| | | | - Ting Zhou
- University at Buffalo, New York, United States
| | - Janos Toth
- University of Debrecen, Faculty of Informatics 4002 Debrecen, POB 400, Hungary
| | - Agnes Baran
- University of Debrecen, Faculty of Informatics 4002 Debrecen, POB 400, Hungary
| | | | | | | | | | - Xingzheng Lyu
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China; Machine Learning for Bioimage Analysis Group, Bioinformatics Institute, A*STAR, Singapore
| | - Li Cheng
- Machine Learning for Bioimage Analysis Group, Bioinformatics Institute, A*STAR, Singapore; Department of Electric and Computer Engineering, University of Alberta, Canada
| | - Qinhao Chu
- School of Computing, National University of Singapore, Singapore
| | - Pengcheng Li
- School of Computing, National University of Singapore, Singapore
| | - Xin Ji
- Beijing Shanggong Medical Technology Co., Ltd., China
| | - Sanyuan Zhang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Yaxin Shen
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China
| | - Ling Dai
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China
| | | | | | - Tânia Melo
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
| | - Teresa Araújo
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FEUP - Faculty of Engineering of the University of Porto, Porto, Portugal
| | - Balazs Harangi
- University of Debrecen, Faculty of Informatics 4002 Debrecen, POB 400, Hungary
| | - Bin Sheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China
| | - Ruogu Fang
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, USA
| | | | - Andras Hajdu
- University of Debrecen, Faculty of Informatics 4002 Debrecen, POB 400, Hungary
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, China
| | - Ana Maria Mendonça
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FEUP - Faculty of Engineering of the University of Porto, Porto, Portugal
| | - Shaoting Zhang
- Department of Computer Science, University of North Carolina at Charlotte, USA
| | - Aurélio Campilho
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FEUP - Faculty of Engineering of the University of Porto, Porto, Portugal
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Luca Giancardo
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA
| | | | - Fabrice Mériaudeau
- Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Malaysia; ImViA/IFTIM, Université de Bourgogne, Dijon, France
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18
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Rana HK, Azam MS, Akhtar MR, Quinn JM, Moni MA. A fast iris recognition system through optimum feature extraction. PeerJ Comput Sci 2019; 5:e184. [PMID: 33816837 PMCID: PMC7924705 DOI: 10.7717/peerj-cs.184] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Accepted: 03/11/2019] [Indexed: 05/25/2023]
Abstract
With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person's lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris template classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.
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Affiliation(s)
- Humayan Kabir Rana
- Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh
| | - Md. Shafiul Azam
- Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh
| | - Mst. Rashida Akhtar
- Department of Computer Science and Engineering, Varendra University, Rajshahi, Bangladesh
| | - Julian M.W. Quinn
- Bone Biology Division, Garvan Institute of Medical Research, NSW, Australia
| | - Mohammad Ali Moni
- Bone Biology Division, Garvan Institute of Medical Research, NSW, Australia
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
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19
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Ajaz A, Kumar H, Aliahmad B, Kumar DK. The relationship between retinal vessel geometrical changes to incidence and progression of Diabetic Macular Edema. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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20
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Koh JEW, Hagiwara Y, Oh SL, Tan JH, Ciaccio EJ, Green PH, Lewis SK, Rajendra Acharya U. Automated diagnosis of celiac disease using DWT and nonlinear features with video capsule endoscopy images. FUTURE GENERATION COMPUTER SYSTEMS 2019; 90:86-93. [DOI: 10.1016/j.future.2018.07.044] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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21
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Amin J, Sharif M, Rehman A, Raza M, Mufti MR. Diabetic retinopathy detection and classification using hybrid feature set. Microsc Res Tech 2018; 81:990-996. [DOI: 10.1002/jemt.23063] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Revised: 04/25/2018] [Accepted: 05/15/2018] [Indexed: 12/25/2022]
Affiliation(s)
- Javeria Amin
- Department of Computer ScienceUniversity of WahPakistan
| | - Muhammad Sharif
- Department of Computer ScienceCOMSATS University Islamabad Wah Campus Pakistan
| | - Amjad Rehman
- College of Computer and Information Systems, Al‐Yamamah University Riyadh 11512 Saudi Arabia
| | - Mudassar Raza
- Department of Computer ScienceCOMSATS University Islamabad Wah Campus Pakistan
| | - Muhammad Rafiq Mufti
- Department of Computer ScienceCOMSATS Institute of Information Technology Vehari Pakistan
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22
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Porwal P, Pachade S, Kokare M, Giancardo L, Mériaudeau F. Retinal image analysis for disease screening through local tetra patterns. Comput Biol Med 2018; 102:200-210. [DOI: 10.1016/j.compbiomed.2018.09.028] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 09/11/2018] [Accepted: 09/26/2018] [Indexed: 11/27/2022]
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23
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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.
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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.
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24
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Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features. Comput Biol Med 2018; 94:11-18. [PMID: 29353161 DOI: 10.1016/j.compbiomed.2017.12.024] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Revised: 12/29/2017] [Accepted: 12/29/2017] [Indexed: 12/12/2022]
Abstract
Liver is the heaviest internal organ of the human body and performs many vital functions. Prolonged cirrhosis and fatty liver disease may lead to the formation of benign or malignant lesions in this organ, and an early and reliable evaluation of these conditions can improve treatment outcomes. Ultrasound imaging is a safe, non-invasive, and cost-effective way of diagnosing liver lesions. However, this technique has limited performance in determining the nature of the lesions. This study initiates a computer-aided diagnosis (CAD) system to aid radiologists in an objective and more reliable interpretation of ultrasound images of liver lesions. In this work, we have employed radon transform and bi-directional empirical mode decomposition (BEMD) to extract features from the focal liver lesions. After which, the extracted features were subjected to particle swarm optimization (PSO) technique for the selection of a set of optimized features for classification. Our automated CAD system can differentiate normal, malignant, and benign liver lesions using machine learning algorithms. It was trained using 78 normal, 26 benign and 36 malignant focal lesions of the liver. The accuracy, sensitivity, and specificity of lesion classification were 92.95%, 90.80%, and 97.44%, respectively. The proposed CAD system is fully automatic as no segmentation of region-of-interest (ROI) is required.
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25
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Wu C, Wang Y, Wang F. Deep Learning for Ovarian Tumor Classification with Ultrasound Images. ADVANCES IN MULTIMEDIA INFORMATION PROCESSING – PCM 2018 2018. [DOI: 10.1007/978-3-030-00764-5_36] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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26
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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
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27
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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.
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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
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28
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TRIPATHY RK, PATERNINA MARIORARRIETA, ARRIETA JUANG, PATTANAIK P. AUTOMATED DETECTION OF ATRIAL FIBRILLATION ECG SIGNALS USING TWO STAGE VMD AND ATRIAL FIBRILLATION DIAGNOSIS INDEX. J MECH MED BIOL 2017. [DOI: 10.1142/s0219519417400449] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Atrial fibrillation (AF) is a common atrial arrhythmia occurring in clinical practice and can be diagnosed using electrocardiogram (ECG) signal. The conventional diagnostic features of ECG signal are not enough to quantify the pathological variations during AF. Therefore, an automated detection of AF pathology using the new diagnostic features of ECG signal is required. This paper proposes a novel method for the detection of AF using ECG signals. In this work, we are using a novel nonlinear method namely, the two-stage variational mode decomposition (VMD) to analyze ECG and deep belief network (DBN) for automated AF detection. First, the ECG signals of both normal sinus rhythm (NSR) and AF classes are decomposed into different modes using VMD. The first mode of VMD is decomposed in the second stage as this mode captures the atrial activity (AA) information during AF. The remaining modes of ECG captures the ventricular activity information. The sample entropy (SE) and the VMD estimated center frequency features are extracted from the sub-modes of AA mode and ventricular activity modes. These extracted features coupled with DBN classifier is able to classify normal and AF ECG signals with an accuracy, sensitivity and specificity values of 98.27%, 97.77% and 98.67%, respectively. We have developed an atrial fibrillation diagnosis index (AFDI) using selected SE and center frequency features to detect AF with a single number. The system is ready to be tested on huge database and can be used in main hospitals to detect AF ECG classes.
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Affiliation(s)
- R. K. TRIPATHY
- Faculty of Engineering and Technology (ITER), S‘O’A University, Bhubaneswar 751030, India
| | - MARIO R. ARRIETA PATERNINA
- Department of Electrical Engineering, National Autonomous University of Mexico, Mexico City 04510, Mexico
| | | | - P. PATTANAIK
- Faculty of Engineering and Technology (ITER), S‘O’A University, Bhubaneswar 751030, India
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29
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KANG YIDA, ZHUO DEMING, FOO RUIENANNE, LIM CHOOMIN, FAUST OLIVER, HAGIWARA YUKI. ALGORITHM FOR THE DETECTION OF CONGESTIVE HEART FAILURE INDEX. J MECH MED BIOL 2017. [DOI: 10.1142/s0219519417400437] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
This study documents our efforts to provide computer support for the diagnosis of congestive heart failure (CHF). That computer support takes the form of an index value. A high index value indicates a low probability of CHF, and an index value below a threshold of 25.6 suggests a high probability of CHF. To create that index, we have designed a sophisticated algorithm chain which takes electrocardiogram signals as input. The signals are pre-processed before they are sent to a range of nonlinear feature extraction algorithms. The top 10 feature extraction methods were used to create the CHF index. By using objective feature extraction algorithms, we avoid the problem of inter- and intra-observer variability. We observed that the nonlinear feature extraction methods reflect the nature of the human heart very well. That observation is based on the fact that the nonlinear features achieved low [Formula: see text]-values and high feature ranking criterion scores.
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Affiliation(s)
- YI DA KANG
- Department of Electronic and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - DEMING ZHUO
- Department of Electronic and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - RUI EN ANNE FOO
- Department of Electronic and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - CHOO MIN LIM
- Department of Electronic and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - OLIVER FAUST
- Department of Engineering and Mathematics, Sheffield Hallam University, UK
| | - YUKI HAGIWARA
- Department of Electronic and Computer Engineering, Ngee Ann Polytechnic, Singapore
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30
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SUDARSHAN VIDYAK, KOH JOELEW, TAN JENHONG, HAGIWARA YUKI, CHUA KUANGCHUA, NG EDDIEYK, TONG LOUIS. PERFORMANCE EVALUATION OF DRY EYE DETECTION SYSTEM USING HIGHER-ORDER SPECTRA FEATURES FOR DIFFERENT NOISE LEVELS IN IR THERMAL IMAGES. J MECH MED BIOL 2017. [DOI: 10.1142/s0219519417400103] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The enhanced tear film evaporation and diminished tear production causes a dry eye (DE) condition. A non-invasive infrared (IR) thermography is most commonly used as a diagnostic tool for diagnosis of DE. However, the availability of high-quality IR thermal camera at low cost is difficult. Hence, an efficient DE detection system which can perform efficiently by using low-cost and low-quality images instead of conventional IR images would be a significant contribution. Therefore, in this work, we have evaluated the performance of automated non-invasive DE detection system using low-quality images obtained by adding different levels of noise to high-quality IR images. In this work, the performances of two non-linear higher-order spectra (HOS) cumulants and bispectrum features are compared. These features are extracted from the IR images with different levels of Gaussian noise. Principal component analysis (PCA) is performed on these extracted features and they are ranked using [Formula: see text]-value and later fed to different classifiers. We have achieved the accuracies, sensitivities and specificities of: (i) 86.90%, 85.71% and 88.10%, with noise level 0 using 24 bispectrum features, and (ii) 80.95%, 85.71% and 76.19%, with noise level 10 using 15 bispectrum features for right eye IR images. This study exhibits that even in the presence of high levels of noise, the detection of DE is possible and our proposed method performs efficiently using HOS bispectrum features. Thus, our proposed method can be used to detect DE using low-quality and inexpensive cameras instead of high-cost IR camera.
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Affiliation(s)
- VIDYA K. SUDARSHAN
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Science, Singapore City 599491, Singapore
- School of Electrical and Computer Engineering, University of Newcastle, Singapore City 038986, Singapore
| | - JOEL E. W. KOH
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore City 599489, Singapore
| | - JEN HONG TAN
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore City 599489, Singapore
| | - YUKI HAGIWARA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore City 599489, Singapore
| | - KUANG CHUA CHUA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore City 599489, Singapore
| | - EDDIE Y. K. NG
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore City 639798, Singapore
| | - LOUIS TONG
- Singapore Eye Research Institute, Singapore City 168751, Singapore
- Singapore National Eye Center, Singapore City 168751, Singapore
- Duke-NUS Graduate Medical School, Singapore City 169857, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore City 117597, Singapore
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31
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MURALIDHAR BAIRY G, NIRANJAN UC, OH SHULIH, KOH JOELEW, SUDARSHAN VIDYAK, TAN JENHONG, HAGIWARA YUKI, NG EDDIEYK. ALCOHOLIC INDEX USING NON-LINEAR FEATURES EXTRACTED FROM DIFFERENT FREQUENCY BANDS. J MECH MED BIOL 2017. [DOI: 10.1142/s0219519417400097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Alcoholism is a complex condition that mainly disturbs the neuronal networks in Central Nervous System (CNS). This disorder not only disturbs the brain, but also affects the behavior, emotions, and cognitive judgements. Electroencephalography (EEG) is a valuable tool to examine the neuropsychiatric disorders like alcoholism. The EEG is a well-established modality to diagnose the electrical activity produced by the populations of neurons in cerebral cortex. However, EEG signals are non-linear in nature; hence very challenging to interpret the valuable information from them using linear methods. Thus, using non-linear methods to analyze EEG signals can be beneficial in order to predict the brain signals condition. This paper presents a computer-aided diagnostic method for the detection of alcoholic EEG signals from normal by employing the non-linear techniques. First, the EEG signals are subjected to six levels of Wavelet Packet Decomposition (WPD) to obtain seven wavebands (delta ([Formula: see text]), theta ([Formula: see text]), lower alpha (la), upper alpha (ua), lower beta (lb), upper beta (ub), lower gamma (lg)). From each wavebands (activity bands), 19 non-linear features such as Recurrence Quantification Analysis (RQA) ([Formula: see text]), Approximate Entropy ([Formula: see text]), Energy ([Formula: see text]), Fractal Dimension (FD) ([Formula: see text]), Permutation Entropy ([Formula: see text]), Detrended Fluctuation Analysis ([Formula: see text]), Hurst Exponent ([Formula: see text]), Largest Lyapunov Exponent ([Formula: see text]), Sample Entropy ([Formula: see text]), Shannon’s Entropy ([Formula: see text]), Renyi’s entropy ([Formula: see text]), Tsalli’s entropy ([Formula: see text]), Fuzzy entropy ([Formula: see text]), Wavelet entropy ([Formula: see text]), Kolmogorov–Sinai entropy ([Formula: see text]), Modified Multiscale Entropy ([Formula: see text]), Hjorth’s parameters (activity ([Formula: see text]), mobility ([Formula: see text]), and complexity ([Formula: see text])) are extracted. The extracted features are then ranked using Bhattacharyya, Entropy, Fuzzy entropy-based Max-Relevancy and Min-Redundancy (mRMR), Receiver Operating Characteristic (ROC), [Formula: see text]-test, and Wilcoxon. These ranked features are given to train Support Vector Machine (SVM) classifier. The SVM classifier with radial basis function (RBF) achieved 95.41% accuracy, 93.33% sensitivity and 97.50% specificity using four non-linear features ranked by Wilcoxon method. In addition, an integrated index called Alcoholic Index (ALCOHOLI) is developed using highly ranked two features for identification of normal and alcoholic EEG signals using a single number. This system is rapid, efficient, and inexpensive and can be employed as an EEG analysis assisting system by clinicians in the detection of alcoholism. In addition, the proposed system can be used in rehabilitation centers to evaluate person with alcoholism over time and observe the outcome of treatment provided for reducing or reversing the impact of the condition on the brain.
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Affiliation(s)
- G. MURALIDHAR BAIRY
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal
| | - U. C. NIRANJAN
- Department of Biomedical Engineering & Electronics and Communication, Adjunct Faculty, Manipal Institute of Technology, Manipal
| | - SHU LIH OH
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - JOEL E. W. KOH
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - VIDYA K. SUDARSHAN
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore
- School of Electrical and Computer Engineering, University of Newcastle, Singapore
| | - JEN HONG TAN
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - YUKI HAGIWARA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - EDDIE Y. K. NG
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
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BHAT SHREYA, ADAM MUHAMMAD, HAGIWARA YUKI, NG EDDIEY. THE BIOPHYSICAL PARAMETER MEASUREMENTS FROM PPG SIGNAL. J MECH MED BIOL 2017. [DOI: 10.1142/s021951941740005x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Early investigation on blood circulation by Hertzman (1937) leads to the observation of vital body signs such as respiration rate, heart rate (HR), blood oxygenation and vascular assessment using photoplethysmographic (PPG) device. PPG is a noninvasive, painless optical technique used to monitor the pulsations linked to alteration in the blood volume. The PPG waveform is a summation of pulsatile and nonpulsatile components and contains useful information about the physiological systems. With the breakthrough in technology and development of powerful analytical tools, PPG devices are constantly being used in advanced medical equipments such as smart-watches and smart-wristbands for HR monitoring, pulse oximeters for measuring respiratory rate and noncontact PPG device for blood oxygen saturation measurement. This paper presents description on PPG and its characteristic waveform and working principle. It also includes brief explanation on nonlinear analysis of PPG signals and salient applications of PPG followed by its advantages and limitations.
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Affiliation(s)
- SHREYA BHAT
- Department of Psychiatry, St John’s Research Institute, Bangalore, India
| | - MUHAMMAD ADAM
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - YUKI HAGIWARA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - EDDIE Y. K. NG
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
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Maheshwari S, Pachori RB, Kanhangad V, Bhandary SV, Acharya UR. Iterative variational mode decomposition based automated detection of glaucoma using fundus images. Comput Biol Med 2017; 88:142-149. [DOI: 10.1016/j.compbiomed.2017.06.017] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2017] [Revised: 06/16/2017] [Accepted: 06/16/2017] [Indexed: 11/16/2022]
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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]
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Sharma M, Deb D, Acharya UR. A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic EEG signals. APPL INTELL 2017. [DOI: 10.1007/s10489-017-1042-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Acharya UR, Bhat S, Koh JEW, Bhandary SV, Adeli H. A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images. Comput Biol Med 2017; 88:72-83. [PMID: 28700902 DOI: 10.1016/j.compbiomed.2017.06.022] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 06/28/2017] [Accepted: 06/28/2017] [Indexed: 01/17/2023]
Abstract
Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Current techniques for computer-aided analysis of the optic nerve and retinal nerve fiber layer (RNFL) are expensive and require keen interpretation by trained specialists. Hence, an automated system is highly desirable for a cost-effective and accurate screening for the diagnosis of glaucoma. This paper presents a new methodology and a computerized diagnostic system. Adaptive histogram equalization is used to convert color images to grayscale images followed by convolution of these images with Leung-Malik (LM), Schmid (S), and maximum response (MR4 and MR8) filter banks. The basic microstructures in typical images are called textons. The convolution process produces textons. Local configuration pattern (LCP) features are extracted from these textons. The significant features are selected using a sequential floating forward search (SFFS) method and ranked using the statistical t-test. Finally, various classifiers are used for classification of images into normal and glaucomatous classes. A high classification accuracy of 95.8% is achieved using six features obtained from the LM filter bank and the k-nearest neighbor (kNN) classifier. A glaucoma integrative index (GRI) is also formulated to obtain a reliable and effective system.
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Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, 599491, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Malaysia.
| | - Shreya Bhat
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal, 576104, India
| | - Joel E W Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Sulatha V Bhandary
- Department of Ophthalmology, Kasturba Medical College, Manipal, 576104, India
| | - Hojjat Adeli
- Departments of Neuroscience, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, United States; Departments of Neurology, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, United States; Departments of Biomedical Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, United States; Departments of Biomedical Informatics, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, United States; Departments of Civil, Environmental, and Geodetic Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, United States
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