51
|
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.
Collapse
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
| |
Collapse
|
52
|
Saba L, Jain PK, Suri HS, Ikeda N, Araki T, Singh BK, Nicolaides A, Shafique S, Gupta A, Laird JR, Suri JS. Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm. J Med Syst 2017; 41:98. [PMID: 28501967 DOI: 10.1007/s10916-017-0745-0] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Accepted: 04/27/2017] [Indexed: 01/18/2023]
Abstract
Severe atherosclerosis disease in carotid arteries causes stenosis which in turn leads to stroke. Machine learning systems have been previously developed for plaque wall risk assessment using morphology-based characterization. The fundamental assumption in such systems is the extraction of the grayscale features of the plaque region. Even though these systems have the ability to perform risk stratification, they lack the ability to achieve higher performance due their inability to select and retain dominant features. This paper introduces a polling-based principal component analysis (PCA) strategy embedded in the machine learning framework to select and retain dominant features, resulting in superior performance. This leads to more stability and reliability. The automated system uses offline image data along with the ground truth labels to generate the parameters, which are then used to transform the online grayscale features to predict the risk of stroke. A set of sixteen grayscale plaque features is computed. Utilizing the cross-validation protocol (K = 10), and the PCA cutoff of 0.995, the machine learning system is able to achieve an accuracy of 98.55 and 98.83%corresponding to the carotidfar wall and near wall plaques, respectively. The corresponding reliability of the system was 94.56 and 95.63%, respectively. The automated system was validated against the manual risk assessment system and the precision of merit for same cross-validation settings and PCA cutoffs are 98.28 and 93.92%for the far and the near wall, respectively.PCA-embedded morphology-based plaque characterization shows a powerful strategy for risk assessment and can be adapted in clinical settings.
Collapse
Affiliation(s)
- Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Pankaj K Jain
- Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA
| | - Harman S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | - Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Bikesh K Singh
- Department of Biomedical Engineering, NIT Raipur, Raipur, Chhattisgarh, India
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, England, UK.,Vascular Diagnostic Centre, University of Cyprus, Nicosia, Cyprus
| | - Shoaib Shafique
- CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA
| | - Ajay Gupta
- Brain and Mind Research Institute and Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | - John R Laird
- UC Davis Vascular Centre, University of California, Davis, CA, USA
| | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA. .,Department of Electrical Engineering, University of Idaho (Affl.), Pocatello, ID, USA.
| |
Collapse
|
53
|
Acharya UR, Mookiah MRK, Koh JE, Tan JH, Bhandary SV, Rao AK, Hagiwara Y, Chua CK, Laude A. Automated diabetic macular edema (DME) grading system using DWT, DCT Features and maculopathy index. Comput Biol Med 2017; 84:59-68. [DOI: 10.1016/j.compbiomed.2017.03.016] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 03/16/2017] [Accepted: 03/17/2017] [Indexed: 12/11/2022]
|
54
|
Computer aided diagnosis of Coronary Artery Disease, Myocardial Infarction and carotid atherosclerosis using ultrasound images: A review. Phys Med 2017; 33:1-15. [DOI: 10.1016/j.ejmp.2016.12.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 11/21/2016] [Accepted: 12/04/2016] [Indexed: 02/08/2023] Open
|
55
|
Yuvaraj R, Rajendra Acharya U, Hagiwara Y. A novel Parkinson’s Disease Diagnosis Index using higher-order spectra features in EEG signals. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2756-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
|
56
|
Araki T, Jain PK, Suri HS, Londhe ND, Ikeda N, El-Baz A, Shrivastava VK, Saba L, Nicolaides A, Shafique S, Laird JR, Gupta A, Suri JS. Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm. Comput Biol Med 2016; 80:77-96. [PMID: 27915126 DOI: 10.1016/j.compbiomed.2016.11.011] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2016] [Revised: 11/20/2016] [Accepted: 11/25/2016] [Indexed: 01/26/2023]
Abstract
Stroke risk stratification based on grayscale morphology of the ultrasound carotid wall has recently been shown to have a promise in classification of high risk versus low risk plaque or symptomatic versus asymptomatic plaques. In previous studies, this stratification has been mainly based on analysis of the far wall of the carotid artery. Due to the multifocal nature of atherosclerotic disease, the plaque growth is not restricted to the far wall alone. This paper presents a new approach for stroke risk assessment by integrating assessment of both the near and far walls of the carotid artery using grayscale morphology of the plaque. Further, this paper presents a scientific validation system for stroke risk assessment. Both these innovations have never been presented before. The methodology consists of an automated segmentation system of the near wall and far wall regions in grayscale carotid B-mode ultrasound scans. Sixteen grayscale texture features are computed, and fed into the machine learning system. The training system utilizes the lumen diameter to create ground truth labels for the stratification of stroke risk. The cross-validation procedure is adapted in order to obtain the machine learning testing classification accuracy through the use of three sets of partition protocols: (5, 10, and Jack Knife). The mean classification accuracy over all the sets of partition protocols for the automated system in the far and near walls is 95.08% and 93.47%, respectively. The corresponding accuracies for the manual system are 94.06% and 92.02%, respectively. The precision of merit of the automated machine learning system when compared against manual risk assessment system are 98.05% and 97.53% for the far and near walls, respectively. The ROC of the risk assessment system for the far and near walls is close to 1.0 demonstrating high accuracy.
Collapse
Affiliation(s)
- Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Pankaj K Jain
- Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA
| | - Harman S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Narendra D Londhe
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, USA
| | | | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, England; Vascular Diagnostic Centre, University of Cyprus, Nicosia, Cyprus
| | - Shoaib Shafique
- CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA
| | - John R Laird
- UC Davis Vascular Centre, University of California, Davis, CA, USA
| | - Ajay Gupta
- Brain and Mind Research Institute and Department of Radiology, Weill Cornell Medical College, NY, USA
| | - Jasjit S Suri
- Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA; Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA.
| |
Collapse
|
57
|
Acharya UR, Mookiah MRK, Koh JEW, Tan JH, Bhandary SV, Rao AK, Fujita H, Hagiwara Y, Chua CK, Laude A. Automated screening system for retinal health using bi-dimensional empirical mode decomposition and integrated index. Comput Biol Med 2016; 75:54-62. [PMID: 27253617 DOI: 10.1016/j.compbiomed.2016.04.015] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Revised: 04/07/2016] [Accepted: 04/22/2016] [Indexed: 11/27/2022]
Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore 599491, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Malaysia
| | | | - Joel E W Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Jen Hong Tan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Sulatha V Bhandary
- Department of Ophthalmology, Kasturba Medical College, Manipal, 576104, India
| | - A Krishna Rao
- Department of Ophthalmology, Kasturba Medical College, Manipal, 576104, India
| | - Hamido Fujita
- Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate, 020-0693, Japan
| | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Chua Kuang Chua
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Augustinus Laude
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, 308433, Singapore
| |
Collapse
|
58
|
Mazher M, Aziz AA, Malik AS. Evaluation of rehearsal effects of multimedia content based on EEG using machine learning algorithms. 2016 6TH INTERNATIONAL CONFERENCE ON INTELLIGENT AND ADVANCED SYSTEMS (ICIAS) 2016. [DOI: 10.1109/icias.2016.7824134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
|
59
|
FAUST OLIVER, YU WENWEI. FORMAL AND MODEL DRIVEN DESIGN OF THE BRIGHT LIGHT THERAPY SYSTEM LUXAMET. J MECH MED BIOL 2016. [DOI: 10.1142/s0219519416500652] [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
Seasonal depression seriously diminishes the quality of life for many patients. To improve their condition, we propose LUXAMET, a bright light therapy system. This system has the potential to relieve patients from some of the symptoms caused by seasonal depression. The system was designed with a formal and model driven design methodology. This methodology enabled us to minimize systemic hazards, like blinding patients with an unhealthy dose of light. This was achieved by controlling race conditions and memory leaks, during design time. We prove that the system specification is deadlock as well as livelock free and there are no invariant violations. These proofs, together with the similarity between specification model and implementation code, make us confident that the implemented system is a reliable tool which can help patients during seasonal depression.
Collapse
Affiliation(s)
- OLIVER FAUST
- School of Engineering, University of Aberdeen, Scotland, UK
| | - WENWEI YU
- School of Science and Engineering, Habib University, Karachi, Pakistan
| |
Collapse
|
60
|
Fok PW. Multi-Layer Mechanical Model of Glagov Remodeling in Coronary Arteries: Differences between In-Vivo and Ex-Vivo Measurements. PLoS One 2016; 11:e0159304. [PMID: 27427954 PMCID: PMC4948909 DOI: 10.1371/journal.pone.0159304] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2016] [Accepted: 06/30/2016] [Indexed: 11/18/2022] Open
Abstract
When blood vessels undergo remodeling because of the buildup of atherosclerotic plaque, it is thought that they first undergo compensatory or outward remodeling, followed by inward remodeling: the lumen area stays roughly constant or increases slightly and then decreases rapidly. The second phase of remodeling is supposed to start after the plaque burden exceeds about 40%. These changes in the vessel were first observed by S. Glagov who examined cross-sections of coronary arteries at different stages of the disease. In this paper, we use a mathematical model based on growth and elasticity theory to verify the main aspects of Glagov’s result. However, both our model and curve-fitting to the data suggest that the critical stenosis is around 20% rather than 40%. Our model and data from the PROSPECT trial also show that Glagov remodeling is qualitatively different depending on whether measurements are taken ex-vivo or in-vivo. Our results suggest that the first outward phase of “Glagov remodeling” is largely absent for in-vivo measurements: that is, the lumen area always decreases as plaque builds up. We advocate that care must be taken when infering how in-vivo vessels remodel from ex-vivo data.
Collapse
Affiliation(s)
- Pak-Wing Fok
- Department of Mathematical Sciences, University of Delaware, Newark, DE 19716, United States of America
- * E-mail:
| |
Collapse
|
61
|
A novel approach to multiclass psoriasis disease risk stratification: Machine learning paradigm. Biomed Signal Process Control 2016. [DOI: 10.1016/j.bspc.2016.04.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
|
62
|
A Computer-Aided Diagnosis System for Measuring Carotid Artery Intima-Media Thickness (IMT) Using Quaternion Vectors. J Med Syst 2016; 40:149. [PMID: 27137786 DOI: 10.1007/s10916-016-0507-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Accepted: 04/19/2016] [Indexed: 10/21/2022]
Abstract
This study aims investigating adjustable distant fuzzy c-means segmentation on carotid Doppler images, as well as quaternion-based convolution filters and saliency mapping procedures. We developed imaging software that will simplify the measurement of carotid artery intima-media thickness (IMT) on saliency mapping images. Additionally, specialists evaluated the present images and compared them with saliency mapping images. In the present research, we conducted imaging studies of 25 carotid Doppler images obtained by the Department of Cardiology at Fırat University. After implementing fuzzy c-means segmentation and quaternion-based convolution on all Doppler images, we obtained a model that can be analyzed easily by the doctors using a bottom-up saliency model. These methods were applied to 25 carotid Doppler images and then interpreted by specialists. In the present study, we used color-filtering methods to obtain carotid color images. Saliency mapping was performed on the obtained images, and the carotid artery IMT was detected and interpreted on the obtained images from both methods and the raw images are shown in Results. Also these results were investigated by using Mean Square Error (MSE) for the raw IMT images and the method which gives the best performance is the Quaternion Based Saliency Mapping (QBSM). 0,0014 and 0,000191 mm(2) MSEs were obtained for artery lumen diameters and plaque diameters in carotid arteries respectively. We found that computer-based image processing methods used on carotid Doppler could aid doctors' in their decision-making process. We developed software that could ease the process of measuring carotid IMT for cardiologists and help them to evaluate their findings.
Collapse
|
63
|
Araki T, Ikeda N, Shukla D, Jain PK, Londhe ND, Shrivastava VK, Banchhor SK, Saba L, Nicolaides A, Shafique S, Laird JR, Suri JS. PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 128:137-158. [PMID: 27040838 DOI: 10.1016/j.cmpb.2016.02.004] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 02/16/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Percutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy. Cardiologists use intravascular ultrasound (IVUS) for screening, risk assessment and stratification of coronary artery disease (CAD). We hypothesize that plaque components are vulnerable to rupture due to plaque progression. Currently, there are no standard grayscale IVUS tools for risk assessment of plaque rupture. This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis (PCA) for plaque feature dimensionality reduction and dominant feature selection technique. The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup. METHOD This system consists of a machine learning paradigm which uses a support vector machine (SVM) combined with PCA for optimal and dominant coronary artery morphological feature extraction. Carotid artery proven intima-media thickness (cIMT) biomarker is adapted as a gold standard during the training phase of the machine learning system. For the performance evaluation, K-fold cross validation protocol is adapted with 20 trials per fold. For choosing the dominant features out of the 56 grayscale features, a polling strategy of PCA is adapted where the original value of the features is unaltered. Different protocols are designed for establishing the stability and reliability criteria of the coronary risk assessment system (cRAS). RESULTS Using the PCA-based machine learning paradigm and cross-validation protocol, a classification accuracy of 98.43% (AUC 0.98) with K=10 folds using an SVM radial basis function (RBF) kernel was achieved. A reliability index of 97.32% and machine learning stability criteria of 5% were met for the cRAS. CONCLUSIONS This is the first Computer aided design (CADx) system of its kind that is able to demonstrate the ability of coronary risk assessment and stratification while demonstrating a successful design of the machine learning system based on our assumptions.
Collapse
Affiliation(s)
- Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | - Devarshi Shukla
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Pankaj K Jain
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Narendra D Londhe
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | | | - Sumit K Banchhor
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, England, United Kingdom; Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus
| | - Shoaib Shafique
- CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA
| | - John R Laird
- UC Davis Vascular Center, University of California, Davis, CA, USA
| | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA; Point-Of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA; Department of Electrical Engineering, University of Idaho (Affl.), Pocatello, ID, USA.
| |
Collapse
|
64
|
Acharya UR, Mookiah MRK, Koh JEW, Tan JH, Noronha K, Bhandary SV, Rao AK, Hagiwara Y, Chua CK, Laude A. Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features. Comput Biol Med 2016; 73:131-40. [PMID: 27107676 DOI: 10.1016/j.compbiomed.2016.04.009] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Revised: 04/10/2016] [Accepted: 04/14/2016] [Indexed: 11/18/2022]
Abstract
Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images. An automated digital fundus photography based screening system can overcome these drawbacks. Such a safe, non-contact and cost-effective platform can be used as a screening system for dry AMD. In this paper, we are proposing a novel algorithm using Radon Transform (RT), Discrete Wavelet Transform (DWT) coupled with Locality Sensitive Discriminant Analysis (LSDA) for automated diagnosis of AMD. First the image is subjected to RT followed by DWT. The extracted features are subjected to dimension reduction using LSDA and ranked using t-test. The performance of various supervised classifiers namely Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) are compared to automatically discriminate to normal and AMD classes using ranked LSDA components. The proposed approach is evaluated using private and public datasets such as ARIA and STARE. The highest classification accuracy of 99.49%, 96.89% and 100% are reported for private, ARIA and STARE datasets. Also, AMD index is devised using two LSDA components to distinguish two classes accurately. Hence, this proposed system can be extended for mass AMD screening.
Collapse
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, SIM University, 599491, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Malaysia
| | | | - Joel E W Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Jen Hong Tan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Kevin Noronha
- Department of Electronics and Telecommunication, St. Francis Institute of Technology, Mumbai 400103, India
| | - Sulatha V Bhandary
- Department of Ophthalmology, Kasturba Medical College, Manipal 576104, India
| | - A Krishna Rao
- Department of Ophthalmology, Kasturba Medical College, Manipal 576104, India
| | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Chua Kuang Chua
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Augustinus Laude
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, 308433, Singapore
| |
Collapse
|
65
|
A Review on Carotid Ultrasound Atherosclerotic Tissue Characterization and Stroke Risk Stratification in Machine Learning Framework. Curr Atheroscler Rep 2016; 17:55. [PMID: 26233633 DOI: 10.1007/s11883-015-0529-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Cardiovascular diseases (including stroke and heart attack) are identified as the leading cause of death in today's world. However, very little is understood about the arterial mechanics of plaque buildup, arterial fibrous cap rupture, and the role of abnormalities of the vasa vasorum. Recently, ultrasonic echogenicity characteristics and morphological characterization of carotid plaque types have been shown to have clinical utility in classification of stroke risks. Furthermore, this characterization supports aggressive and intensive medical therapy as well as procedures, including endarterectomy and stenting. This is the first state-of-the-art review to provide a comprehensive understanding of the field of ultrasonic vascular morphology tissue characterization. This paper presents fundamental and advanced ultrasonic tissue characterization and feature extraction methods for analyzing plaque. Additionally, the paper shows how the risk stratification is achieved using machine learning paradigms. More advanced methods need to be developed which can segment the carotid artery walls into multiple regions such as the bulb region and areas both proximal and distal to the bulb. Furthermore, multimodality imaging is needed for validation of such advanced methods for stroke and cardiovascular risk stratification.
Collapse
|
66
|
Data mining framework for identification of myocardial infarction stages in ultrasound: A hybrid feature extraction paradigm (PART 2). Comput Biol Med 2016; 71:241-51. [DOI: 10.1016/j.compbiomed.2016.01.029] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Revised: 01/14/2016] [Accepted: 01/30/2016] [Indexed: 02/04/2023]
|
67
|
FAUST OLIVER, NG EYK. COMPUTER AIDED DIAGNOSIS FOR CARDIOVASCULAR DISEASES BASED ON ECG SIGNALS: A SURVEY. J MECH MED BIOL 2016. [DOI: 10.1142/s0219519416400017] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The interpretation of Electroencephalography (ECG) signals is difficult, because even subtle changes in the waveform can indicate a serious heart disease. Furthermore, these waveform changes might not be present all the time. As a consequence, it takes years of training for a medical practitioner to become an expert in ECG-based cardiovascular disease diagnosis. That training is a major investment in a specific skill. Even with expert ability, the signal interpretation takes time. In addition, human interpretation of ECG signals causes interoperator and intraoperator variability. ECG-based Computer-Aided Diagnosis (CAD) holds the promise of improving the diagnosis accuracy and reducing the cost. The same ECG signal will result in the same diagnosis support regardless of time and place. This paper introduces both the techniques used to realize the CAD functionality and the methods used to assess the established functionality. This survey aims to instill trust in CAD of cardiovascular diseases using ECG signals by introducing both a conceptional overview of the system and the necessary assessment methods.
Collapse
Affiliation(s)
- OLIVER FAUST
- Faculty of Arts, Computing, Engineering and Sciences, Sheffield Hallam University, Sheffield, UK
| | - E. Y. K. NG
- School of Mechanical & Aerospace Engineering, College of Engineering, Nanyang Technological University, Singapore
| |
Collapse
|
68
|
ROSATI SAMANTA, MEIBURGER KRISTENMARIKO, BALESTRA GABRIELLA, ACHARYA URAJENDRA, MOLINARI FILIPPO. CAROTID WALL MEASUREMENT AND ASSESSMENT BASED ON PIXEL-BASED AND LOCAL TEXTURE DESCRIPTORS. J MECH MED BIOL 2016. [DOI: 10.1142/s0219519416400066] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Aim of this paper is to develop an automated system for the classification and characterization of carotid wall status and to develop a robust system based on local texture descriptors. A database of 200 longitudinal ultrasound images of carotid artery is used. One-hundred images with Intima-Media Thickness (IMT) value higher than 0.8[Formula: see text]mm are considered as high risk. Six different rectangular pixel neighborhoods were considered: four areas centered on the selected element, with sizes [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] pixels, and two noncentered areas with sizes [Formula: see text] pixels upwards and downwards. We have extracted various texture descriptors (31 based on the co-occurrence gray level matrix, 13 based on the spatial gray level dependence matrix, and 20 based on the gray level run length matrix (GLRLM) from neighborhood. We have used Quick Reduct Algorithm to select 12 most discriminant features from extracted 211 features. Each pixel is then assigned to the vessel lumen, to the intima-media complex, or to the adventitia by using an integrated system of three feed-forward neural networks. The boundaries between the three regions are used to estimate the IMT value. The texture features associated with GLRLM are found to be clinically most significant. We have obtained an overall classification accuracy of 79.5%, sensitivity of 87%, and specificity of 72%. We observed a unique classification pattern between low risk and high risk images: in the latter ones, a considerable number of pixels of the intima–media complex ([Formula: see text]) was classified as belonging to the adventitia. This percentage is statistically higher than that of low risk images ([Formula: see text]; [Formula: see text]). Locally extracted and pixel-based descriptors are able to capture the inner characteristics of the carotid wall. The presence of misclassified pixels in the intima–media complex is associated to higher cardiovascular risk.
Collapse
Affiliation(s)
- SAMANTA ROSATI
- Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | | | - GABRIELLA BALESTRA
- Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - U. RAJENDRA ACHARYA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia
- Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore
| | - FILIPPO MOLINARI
- Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| |
Collapse
|
69
|
ACHARYA URAJENDRA, FUJITA HAMIDO, BHAT SHREYA, KOH JOELEW, ADAM MUHAMMAD, GHISTA DHANJOON, SUDARSHAN VIDYAK, CHUA KOKPOO, CHUA KUANGCHUA, MOLINARI FILIPPO, NG EYK, TAN RUSAN. AUTOMATED DIAGNOSIS OF DIABETES USING ENTROPIES AND DIABETIC INDEX. J MECH MED BIOL 2016. [DOI: 10.1142/s021951941640008x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Diabetes Mellitus (DM) is a chronic metabolic disorder that hampers the body’s energy absorption capacity from the food. It is either caused by pancreatic malfunctioning or the body cells being inactive to insulin production. Prolonged diabetes results in severe complications, such as retinopathy, neuropathy, cardiomyopathy and cardiovascular diseases. DM is an incurable disorder. Thus, diagnosis and monitoring of diabetes is essential to prevent the body organs from severe damage. Heart Rate Variability (HRV) signal processing can be used as one of the methods for the diagnosis of DM. Our paper introduces a noninvasive technique of automated diabetic diagnosis using HRV signals. The R-R interval signals are decomposed using Shearlet transforms integrated with Continuous Wavelet Transform (CWT), and their characteristic features are extracted by using Shannon’s, Renyi’s, Kapur entropies, energy and Higher Order Spectra (HOS). Then, Locality Sensitive Discriminant Analysis (LSDA) is employed to remove insignificant features and reduce the number of employed features. These redundant features are eliminated by using six feature selection algorithms: Student’s t-test, Receiver Operating Characteristic Curve (ROC), Wilcoxon signed-rank test, Bhattacharyya distance, Information entropy and Fuzzy Max-Relevance and Min-Redundancy (MRMR). This step is followed by classification of normal and diabetic signals using different classifiers, such as discriminant classifiers, Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Naïve Bayes (NB), Fuzzy Sugeno (FSC), Gaussian Mixture Model (GMM), AdaBoost and k-Nearest Neighbor (k-NN) classifier. In these classifiers, the selected features are employed to distinguish diabetic signals from normal signals. These classifiers are trained and then tested to validate their accuracy to make accurate diagnosis. The FSC classifier is shown to have the highest (100%) accuracy. Nevertheless, we go one step further in formulating another novel classifier in the form of the diabetic index, and have shown how distinctly it is able to separate diabetic signals from normal signals.
Collapse
Affiliation(s)
- U. RAJENDRA ACHARYA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia
| | - HAMIDO FUJITA
- Iwate Prefectural University (IPU), Faculty of Software and Information Science, Iwate, Japan
| | - SHREYA BHAT
- Department of Psychiatry, St. John’s Research Institute, Bangalore 560034, India
| | - JOEL EW KOH
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - MUHAMMAD ADAM
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | | | - VIDYA K. SUDARSHAN
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - KOK POO CHUA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - KUANG CHUA CHUA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - FILIPPO MOLINARI
- Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - E. Y. K. NG
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
| | - RU SAN TAN
- Department of Cardiology, National Heart Centre, Singapore
| |
Collapse
|
70
|
Sudarshan VK, Acharya UR, Ng EYK, Tan RS, Chou SM, Ghista DN. An integrated index for automated detection of infarcted myocardium from cross-sectional echocardiograms using texton-based features (Part 1). Comput Biol Med 2016; 71:231-40. [PMID: 26898671 DOI: 10.1016/j.compbiomed.2016.01.028] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Revised: 01/14/2016] [Accepted: 01/30/2016] [Indexed: 11/15/2022]
Abstract
Cross-sectional view echocardiography is an efficient non-invasive diagnostic tool for characterizing Myocardial Infarction (MI) and stages of expansion leading to heart failure. An automated computer-aided technique of cross-sectional echocardiography feature assessment can aid clinicians in early and more reliable detection of MI patients before subsequent catastrophic post-MI medical conditions. Therefore, this paper proposes a novel Myocardial Infarction Index (MII) to discriminate infarcted and normal myocardium using features extracted from apical cross-sectional views of echocardiograms. The cross-sectional view of normal and MI echocardiography images are represented as textons using Maximum Responses (MR8) filter banks. Fractal Dimension (FD), Higher-Order Statistics (HOS), Hu's moments, Gabor Transform features, Fuzzy Entropy (FEnt), Energy, Local binary Pattern (LBP), Renyi's Entropy (REnt), Shannon's Entropy (ShEnt), and Kapur's Entropy (KEnt) features are extracted from textons. These features are ranked using t-test and fuzzy Max-Relevancy and Min-Redundancy (mRMR) ranking methods. Then, combinations of highly ranked features are used in the formulation and development of an integrated MII. This calculated novel MII is used to accurately and quickly detect infarcted myocardium by using one numerical value. Also, the highly ranked features are subjected to classification using different classifiers for the characterization of normal and MI LV ultrasound images using a minimum number of features. Our current technique is able to characterize MI with an average accuracy of 94.37%, sensitivity of 91.25% and specificity of 97.50% with 8 apical four chambers view features extracted from only single frame per patient making this a more reliable and accurate classification.
Collapse
Affiliation(s)
- Vidya K Sudarshan
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore; Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia
| | - E Y K Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
| | - Ru San Tan
- Department of Cardiology, National Heart Centre, Singapore
| | - Siaw Meng Chou
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
| | | |
Collapse
|
71
|
Araki T, Ikeda N, Shukla D, Londhe ND, Shrivastava VK, Banchhor SK, Saba L, Nicolaides A, Shafique S, Laird JR, Suri JS. A new method for IVUS-based coronary artery disease risk stratification: A link between coronary & carotid ultrasound plaque burdens. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 124:161-179. [PMID: 26707374 DOI: 10.1016/j.cmpb.2015.10.022] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Revised: 10/12/2015] [Accepted: 10/21/2015] [Indexed: 06/05/2023]
Abstract
Interventional cardiologists have a deep interest in risk stratification prior to stenting and percutaneous coronary intervention (PCI) procedures. Intravascular ultrasound (IVUS) is most commonly adapted for screening, but current tools lack the ability for risk stratification based on grayscale plaque morphology. Our hypothesis is based on the genetic makeup of the atherosclerosis disease, that there is evidence of a link between coronary atherosclerosis disease and carotid plaque built up. This novel idea is explored in this study for coronary risk assessment and its classification of patients between high risk and low risk. This paper presents a strategy for coronary risk assessment by combining the IVUS grayscale plaque morphology and carotid B-mode ultrasound carotid intima-media thickness (cIMT) - a marker of subclinical atherosclerosis. Support vector machine (SVM) learning paradigm is adapted for risk stratification, where both the learning and testing phases use tissue characteristics derived from six feature combinational spaces, which are then used by the SVM classifier with five different kernels sets. These six feature combinational spaces are designed using 56 novel feature sets. K-fold cross validation protocol with 10 trials per fold is used for optimization of best SVM-kernel and best feature combination set. IRB approved coronary IVUS and carotid B-mode ultrasound were jointly collected on 15 patients (2 days apart) via: (a) 40MHz catheter utilizing iMap (Boston Scientific, Marlborough, MA, USA) with 2865 frames per patient (42,975 frames) and (b) linear probe B-mode carotid ultrasound (Toshiba scanner, Japan). Using the above protocol, the system shows the classification accuracy of 94.95% and AUC of 0.95 using optimized feature combination. This is the first system of its kind for risk stratification as a screening tool to prevent excessive cost burden and better patients' cardiovascular disease management, while validating our two hypotheses.
Collapse
Affiliation(s)
- Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | - Devarshi Shukla
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA
| | - Narendra D Londhe
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA
| | | | - Sumit K Banchhor
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, England, United Kingdom; Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus
| | | | - John R Laird
- UC Davis Vascular Center, University of California, Davis, CA, USA
| | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA.
| |
Collapse
|
72
|
Acharya UR, Bhat S, Faust O, Adeli H, Chua ECP, Lim WJE, Koh JEW. Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection. Eur Neurol 2015; 74:268-87. [DOI: 10.1159/000441975] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 10/27/2015] [Indexed: 11/19/2022]
|
73
|
Mazher M, Aziz AA, Malik AS, Qayyum A. A statistical analysis on learning and non-learning mental states using EEG. 2015 IEEE STUDENT SYMPOSIUM IN BIOMEDICAL ENGINEERING & SCIENCES (ISSBES) 2015. [DOI: 10.1109/issbes.2015.7435889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
|
74
|
MOOKIAH MUTHURAMAKRISHNAN, TAN JENHONG, CHUA CHUAKUANG, NG EYK, LAUDE AUGUSTINUS, TONG LOUIS. AUTOMATED CHARACTERIZATION AND DETECTION OF DIABETIC RETINOPATHY USING TEXTURE MEASURES. J MECH MED BIOL 2015. [DOI: 10.1142/s0219519415500451] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The chronic and uncontrolled diabetes mellitus (DM) damages the retinal blood vessels leading to diabetic retinopathy (DR). The advanced stage of DR leads to loss of vision and subsequently blindness. The morphological changes during the progression of DR can be diagnosed using digital fundus images. The pathological changes in the retina influence the variations in pixel patterns which can be quantified using texture measures. In this paper, we have explored different texture measures namely statistical moments, gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), local binary pattern (LBP), laws mask energy (LME), fractal dimension (FD), fourier spectrum (FS) and Gabor wavelet to characterize and classify the normal and DR classes. We have tabulated 109 texture parameters for the normal and DR classes. Further, these features were subjected to empirical receiver operating characteristic (ROC) based ranking to select optimal feature set. The ranked nested features were fed to the support vector machine (SVM) classifier with different kernel functions to evaluate the highest performance measure using the least number of features to discriminate normal and DR classes. Our proposed system was evaluated using two different databases Kasturba Medical College Hospital (KMCH) and Tan Tock Seng Hospital (TTSH), each with 340 images (170 normal and 170 DR). We have also formulated an integrated index called as diabetic retinopathy risk index (DRRI) using selected texture features to discriminate normal and DR classes using single number. The proposed frame work can be used to help the clinicians and also for mass DR screening programs.
Collapse
Affiliation(s)
| | - JEN HONG TAN
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - CHUA KUANG CHUA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - E. Y. K. NG
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - AUGUSTINUS LAUDE
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - LOUIS TONG
- Singapore National Eye Center, Singapore 168751, Singapore
- Ocular Surface Research Group, Singapore Eye Research Institute, Singapore 168751, Singapore
- Duke-NUS Graduate Medical School, Singapore 169857, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| |
Collapse
|
75
|
Pedro LM, Sanches JM, Seabra J, Suri JS, Fernandes E Fernandes J. Asymptomatic carotid disease--a new tool for assessing neurological risk. Echocardiography 2013; 31:353-61. [PMID: 24117920 DOI: 10.1111/echo.12348] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Active carotid plaques are associated with atheroembolism and neurological events; its identification is crucial for stroke prevention. High-definition ultrasound (HDU) can be used to recognize plaque structure in carotid bifurcation stenosis associated with plaque vulnerability and occurrence of brain ischemic events. A new computer-assisted HDU method to study the echomorphology of the carotid plaque and to determine a risk score for developing appropriate symptoms is proposed in this study. Plaque echomorphology characteristics such as presence of ulceration at the plaque surface, juxta-luminal location of echolucent areas, echoheterogeneity were obtained from B-mode ultrasound scans using several image processing algorithms and were combined with measurement of severity of stenosis to obtain a clinical score--enhanced activity index (EAI)--which was correlated with the presence or absence of ipsilateral appropriate ischemic symptoms. An optimal cutoff value of EAI was determined to obtain the best separation between symptomatic (active) from asymptomatic (inactive) plaques and its diagnostic yield was compared to other 2 reference methods by means of receiver-operating characteristic (ROC) analysis. Classification performance was evaluated by leave-one-patient-out cross-validation applied to a cohort of 146 carotid plaques from 99 patients. The proposed method was benchmarked against (a) degree of stenosis criteria and (b) earlier proposed activity index (AI) and demonstrated that EAI yielded the highest accuracy up to an accuracy of 77% to predict asymptomatic plaques that developed symptoms in a prospective cross-sectional study. Enhanced activity index is a noninvasive, easy to obtain parameter, which provided accurate estimation of neurological risk of carotid plaques.
Collapse
Affiliation(s)
- Luís M Pedro
- Faculty of Medicine, Lisbon Academic Medical Centre, University of Lisbon and Lisbon Cardiovascular Institute, Lisbon, Portugal
| | | | | | | | | |
Collapse
|
76
|
Acharya UR, S VS, Molinari F, Saba L, Nicolaides A, Shafique S, Suri JS. Carotid ultrasound symptomatology using atherosclerotic plaque characterization: a class of Atheromatic systems. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:3199-202. [PMID: 23366606 DOI: 10.1109/embc.2012.6346645] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
In this paper, we present a Computer Aided Diagnosis (CAD) based technique (Atheromatic system) for classification of carotid plaques in B-mode ultrasound images into symptomatic or asymptomatic classes. This system, called Atheromatic, has two steps: (i) feature extraction using a combination of Discrete Wavelet Transform (DWT) and averaging algorithms and (ii) classification using Support Vector Machine (SVM) classifier for automated decision making. The CAD system was built and tested using a database consisting of 150 asymptomatic and 196 symptomatic plaque regions of interests which were manually segmented. The ground truth of each plaque was determined based on the presence or absence of symptoms. Three-fold cross-validation protocol was adapted for developing and testing the classifiers. The SVM classifier with a polynomial kernel of order 2 recorded the highest classification accuracy of 83.7%. In the clinical scenario, such a technique, after much more validation, can be used as an adjunct tool to aid physicians by giving a second opinion on the nature of the plaque (symptomatic/asymptomatic) which would help in the more confident determination of the subsequent treatment regime for the patient.
Collapse
Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | | | | | | | | | | | | |
Collapse
|
77
|
Acharya UR, Sree SV, Mookiah MRK, Saba L, Gao H, Mallarini G, Suri JS. Computed tomography carotid wall plaque characterization using a combination of discrete wavelet transform and texture features: A pilot study. Proc Inst Mech Eng H 2013; 227:643-54. [PMID: 23636747 DOI: 10.1177/0954411913480622] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In 30% of stroke victims, the cause of stroke has been found to be the stenosis caused by plaques in the carotid artery. Early detection of plaque and subsequent classification of the same into symptomatic and asymptomatic can help the clinicians to choose only those patients who are at a higher risk of stroke for risky surgeries and stenosis treatments. Therefore, in this work, we have proposed a non-invasive computer-aided diagnostic technique to classify the detected plaque into the two classes. Computed tomography (CT) images of the carotid artery images were used to extract Local Binary Pattern (LBP) features and wavelet energy features. Significant features were then used to train and test several supervised learning algorithm based classifiers. The Support Vector Machine (SVM) classifier with various kernel configurations was evaluated using LBP and wavelet features. The SVM classifier presented the highest accuracy of 88%, sensitivity of 90.2%, and specificity of 86.5% for radial basis function (RBF) kernel function. The CT images of the carotid artery provide unique 3D images of the artery and plaque that could be used for calculating percentage of stenosis. Our proposed technique enables automatic classification of plaque into asymptomatic and symptomatic with high accuracy, and hence, it can be used for deciding the course of treatment. We have also proposed a single-valued integrated index (Atheromatic Index) using the significant features which can provide a more objective and faster prediction of the class.
Collapse
Affiliation(s)
- U R Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore.
| | | | | | | | | | | | | |
Collapse
|
78
|
Acharya UR, Mookiah MRK, Vinitha Sree S, Afonso D, Sanches J, Shafique S, Nicolaides A, Pedro LM, Fernandes E Fernandes J, Suri JS. Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment. Med Biol Eng Comput 2013; 51:513-23. [PMID: 23292291 DOI: 10.1007/s11517-012-1019-0] [Citation(s) in RCA: 80] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2012] [Accepted: 12/17/2012] [Indexed: 11/25/2022]
Abstract
In the case of carotid atherosclerosis, to avoid unnecessary surgeries in asymptomatic patients, it is necessary to develop a technique to effectively differentiate symptomatic and asymptomatic plaques. In this paper, we have presented a data mining framework that characterizes the textural differences in these two classes using several grayscale features based on a novel combination of trace transform and fuzzy texture. The features extracted from the delineated plaque regions in B-mode ultrasound images were used to train several classifiers in order to prepare them for classification of new test plaques. Our CAD system was evaluated using two different databases consisting of 146 (44 symptomatic to 102 asymptomatic) and 346 (196 symptomatic and 150 asymptomatic) images. Both these databases differ in the way the ground truth was determined. We obtained classification accuracies of 93.1 and 85.3 %, respectively. The techniques are low cost, easily implementable, objective, and non-invasive. For more objective analysis, we have also developed novel integrated indices using a combination of significant features.
Collapse
Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi, Singapore.
| | | | | | | | | | | | | | | | | | | |
Collapse
|
79
|
Faust O, Acharya UR, Tamura T. Formal Design Methods for Reliable Computer-Aided Diagnosis: A Review. IEEE Rev Biomed Eng 2012; 5:15-28. [DOI: 10.1109/rbme.2012.2184750] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
80
|
Acharya UR, S VS, M MRK, Saba L, Molinari F, Shafique S, Nicolaides A, Suri JS. Carotid far wall characterization using LBP, Laws' Texture Energy and wall variability: a novel class of Atheromatic systems. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2012:448-451. [PMID: 23365925 DOI: 10.1109/embc.2012.6345964] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
In this work, we present a Computer Aided Diagnostic (CAD) technique (a class of Atheromatic systems) that classifies the automatically segmented carotid far wall Intima-Media Thickness (IMT) regions along the common carotid artery into symptomatic and asymptomatic classes. We extracted texture features based on Local Binary Patterns (LBP) and Law's Texture Energy (LTE) and used the significant features to train and test the Support Vector Machine classifier. We developed the classifiers using three-fold stratified cross validation data resampling technique on 342 IMT wall regions. An accuracy of 89.5% was registered. Thus, the proposed technique is accurate, robust, non-invasive, fast, objective, and cost-effective, and hence, will add more value to the existing carotid plaque diagnostics protocol.
Collapse
Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | | | | | | | | | | | | | | |
Collapse
|
81
|
Computer-Assisted Diagnosis of Tuberculosis: A First Order Statistical Approach to Chest Radiograph. J Med Syst 2011; 36:2751-9. [DOI: 10.1007/s10916-011-9751-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2011] [Accepted: 06/15/2011] [Indexed: 11/27/2022]
|