Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies
Vision is paramount to humans to lead an active personal and professional life. The prevalence of ocular diseases is rising, and diseases such as glaucoma, Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are the leading causes of blindness in developed countries. Identifying the...
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| Published in | Computers in biology and medicine Vol. 84; pp. 89 - 97 |
|---|---|
| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.05.2017
Elsevier Limited |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0010-4825 1879-0534 1879-0534 |
| DOI | 10.1016/j.compbiomed.2017.03.008 |
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| Abstract | Vision is paramount to humans to lead an active personal and professional life. The prevalence of ocular diseases is rising, and diseases such as glaucoma, Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are the leading causes of blindness in developed countries. Identifying these diseases in mass screening programmes is time-consuming, labor-intensive and the diagnosis can be subjective. The use of an automated computer aided diagnosis system will reduce the time taken for analysis and will also reduce the inter-observer subjective variabilities in image interpretation. In this work, we propose one such system for the automatic classification of normal from abnormal (DR, AMD, glaucoma) images. We had a total of 404 normal and 1082 abnormal fundus images in our database. As the first step, 2D-Continuous Wavelet Transform (CWT) decomposition on the fundus images of two classes was performed. Subsequently, energy features and various entropies namely Yager, Renyi, Kapoor, Shannon, and Fuzzy were extracted from the decomposed images. Then, adaptive synthetic sampling approach was applied to balance the normal and abnormal datasets. Next, the extracted features were ranked according to the significances using Particle Swarm Optimization (PSO). Thereupon, the ranked and selected features were used to train the random forest classifier using stratified 10-fold cross validation. Overall, the proposed system presented a performance rate of 92.48%, and a sensitivity and specificity of 89.37% and 95.58% respectively using 15 features. This novel system shows promise in detecting abnormal fundus images, and hence, could be a valuable adjunct eye health screening tool that could be employed in polyclinics, and thereby reduce the workload of specialists at hospitals.
[Display omitted]
•Classification of normal and abnormal (AMD, DR and glaucoma) using fundus images.•Energy and entropy features are extracted from 2D- CWT coefficients.•Implemented ADASYN to synthetically generate images for normal class.•Obtained an accuracy of 92.48%, sensitivity of 89.37% and specificity of 95.58%. |
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| AbstractList | Abstract Vision is paramount to humans to lead an active personal and professional life. The prevalence of ocular diseases is rising, and diseases such as glaucoma, Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are the leading causes of blindness in developed countries. Identifying these diseases in mass screening programmes is time-consuming, labor-intensive and the diagnosis can be subjective. The use of an automated computer aided diagnosis system will reduce the time taken for analysis and will also reduce the inter-observer subjective variabilities in image interpretation. In this work, we propose one such system for the automatic classification of normal from abnormal (DR, AMD, glaucoma) images. We had a total of 404 normal and 1082 abnormal fundus images in our database. As the first step, 2D-Continuous Wavelet Transform (CWT) decomposition on the fundus images of two classes was performed. Subsequently, energy features and various entropies namely Yager, Renyi, Kapoor, Shannon, and Fuzzy were extracted from the decomposed images. Then, adaptive synthetic sampling approach was applied to balance the normal and abnormal datasets. Next, the extracted features were ranked according to the significances using Particle Swarm Optimization (PSO). Thereupon, the ranked and selected features were used to train the random forest classifier using stratified 10-fold cross validation. Overall, the proposed system presented a performance rate of 92.48%, and a sensitivity and specificity of 89.37% and 95.58% respectively using 15 features. This novel system shows promise in detecting abnormal fundus images, and hence, could be a valuable adjunct eye health screening tool that could be employed in polyclinics, and thereby reduce the workload of specialists at hospitals. Vision is paramount to humans to lead an active personal and professional life. The prevalence of ocular diseases is rising, and diseases such as glaucoma, Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are the leading causes of blindness in developed countries. Identifying these diseases in mass screening programmes is time-consuming, labor-intensive and the diagnosis can be subjective. The use of an automated computer aided diagnosis system will reduce the time taken for analysis and will also reduce the inter-observer subjective variabilities in image interpretation. In this work, we propose one such system for the automatic classification of normal from abnormal (DR, AMD, glaucoma) images. We had a total of 404 normal and 1082 abnormal fundus images in our database. As the first step, 2D-Continuous Wavelet Transform (CWT) decomposition on the fundus images of two classes was performed. Subsequently, energy features and various entropies namely Yager, Renyi, Kapoor, Shannon, and Fuzzy were extracted from the decomposed images. Then, adaptive synthetic sampling approach was applied to balance the normal and abnormal datasets. Next, the extracted features were ranked according to the significances using Particle Swarm Optimization (PSO). Thereupon, the ranked and selected features were used to train the random forest classifier using stratified 10-fold cross validation. Overall, the proposed system presented a performance rate of 92.48%, and a sensitivity and specificity of 89.37% and 95.58% respectively using 15 features. This novel system shows promise in detecting abnormal fundus images, and hence, could be a valuable adjunct eye health screening tool that could be employed in polyclinics, and thereby reduce the workload of specialists at hospitals. Vision is paramount to humans to lead an active personal and professional life. The prevalence of ocular diseases is rising, and diseases such as glaucoma, Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are the leading causes of blindness in developed countries. Identifying these diseases in mass screening programmes is time-consuming, labor-intensive and the diagnosis can be subjective. The use of an automated computer aided diagnosis system will reduce the time taken for analysis and will also reduce the inter-observer subjective variabilities in image interpretation. In this work, we propose one such system for the automatic classification of normal from abnormal (DR, AMD, glaucoma) images. We had a total of 404 normal and 1082 abnormal fundus images in our database. As the first step, 2D-Continuous Wavelet Transform (CWT) decomposition on the fundus images of two classes was performed. Subsequently, energy features and various entropies namely Yager, Renyi, Kapoor, Shannon, and Fuzzy were extracted from the decomposed images. Then, adaptive synthetic sampling approach was applied to balance the normal and abnormal datasets. Next, the extracted features were ranked according to the significances using Particle Swarm Optimization (PSO). Thereupon, the ranked and selected features were used to train the random forest classifier using stratified 10-fold cross validation. Overall, the proposed system presented a performance rate of 92.48%, and a sensitivity and specificity of 89.37% and 95.58% respectively using 15 features. This novel system shows promise in detecting abnormal fundus images, and hence, could be a valuable adjunct eye health screening tool that could be employed in polyclinics, and thereby reduce the workload of specialists at hospitals.Vision is paramount to humans to lead an active personal and professional life. The prevalence of ocular diseases is rising, and diseases such as glaucoma, Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are the leading causes of blindness in developed countries. Identifying these diseases in mass screening programmes is time-consuming, labor-intensive and the diagnosis can be subjective. The use of an automated computer aided diagnosis system will reduce the time taken for analysis and will also reduce the inter-observer subjective variabilities in image interpretation. In this work, we propose one such system for the automatic classification of normal from abnormal (DR, AMD, glaucoma) images. We had a total of 404 normal and 1082 abnormal fundus images in our database. As the first step, 2D-Continuous Wavelet Transform (CWT) decomposition on the fundus images of two classes was performed. Subsequently, energy features and various entropies namely Yager, Renyi, Kapoor, Shannon, and Fuzzy were extracted from the decomposed images. Then, adaptive synthetic sampling approach was applied to balance the normal and abnormal datasets. Next, the extracted features were ranked according to the significances using Particle Swarm Optimization (PSO). Thereupon, the ranked and selected features were used to train the random forest classifier using stratified 10-fold cross validation. Overall, the proposed system presented a performance rate of 92.48%, and a sensitivity and specificity of 89.37% and 95.58% respectively using 15 features. This novel system shows promise in detecting abnormal fundus images, and hence, could be a valuable adjunct eye health screening tool that could be employed in polyclinics, and thereby reduce the workload of specialists at hospitals. Vision is paramount to humans to lead an active personal and professional life. The prevalence of ocular diseases is rising, and diseases such as glaucoma, Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are the leading causes of blindness in developed countries. Identifying these diseases in mass screening programmes is time-consuming, labor-intensive and the diagnosis can be subjective. The use of an automated computer aided diagnosis system will reduce the time taken for analysis and will also reduce the inter-observer subjective variabilities in image interpretation. In this work, we propose one such system for the automatic classification of normal from abnormal (DR, AMD, glaucoma) images. We had a total of 404 normal and 1082 abnormal fundus images in our database. As the first step, 2D-Continuous Wavelet Transform (CWT) decomposition on the fundus images of two classes was performed. Subsequently, energy features and various entropies namely Yager, Renyi, Kapoor, Shannon, and Fuzzy were extracted from the decomposed images. Then, adaptive synthetic sampling approach was applied to balance the normal and abnormal datasets. Next, the extracted features were ranked according to the significances using Particle Swarm Optimization (PSO). Thereupon, the ranked and selected features were used to train the random forest classifier using stratified 10-fold cross validation. Overall, the proposed system presented a performance rate of 92.48%, and a sensitivity and specificity of 89.37% and 95.58% respectively using 15 features. This novel system shows promise in detecting abnormal fundus images, and hence, could be a valuable adjunct eye health screening tool that could be employed in polyclinics, and thereby reduce the workload of specialists at hospitals. [Display omitted] •Classification of normal and abnormal (AMD, DR and glaucoma) using fundus images.•Energy and entropy features are extracted from 2D- CWT coefficients.•Implemented ADASYN to synthetically generate images for normal class.•Obtained an accuracy of 92.48%, sensitivity of 89.37% and specificity of 95.58%. |
| Author | Bhandary, Sulatha V. Hagiwara, Yuki Tan, Jen Hong Chua, Kuang Chua Laude, Augustinus Tong, Louis Raghavendra, U. Acharya, U. Rajendra Sree, S. Vinitha Rao, A. Krishna Koh, Joel E.W. Sivaprasad, Sobha |
| Author_xml | – sequence: 1 givenname: Joel E.W. surname: Koh fullname: Koh, Joel E.W. organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489 Singapore, Singapore – sequence: 2 givenname: U. Rajendra surname: Acharya fullname: Acharya, U. Rajendra email: aru@np.edu.sg organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489 Singapore, Singapore – sequence: 3 givenname: Yuki surname: Hagiwara fullname: Hagiwara, Yuki organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489 Singapore, Singapore – sequence: 4 givenname: U. surname: Raghavendra fullname: Raghavendra, U. organization: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal University, Manipal 576104, India – sequence: 5 givenname: Jen Hong surname: Tan fullname: Tan, Jen Hong organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489 Singapore, Singapore – sequence: 6 givenname: S. Vinitha surname: Sree fullname: Sree, S. Vinitha organization: Visiting Scientist, Global Biomedical Technologies, CA, USA – sequence: 7 givenname: Sulatha V. surname: Bhandary fullname: Bhandary, Sulatha V. organization: Department of Ophthalmology, Kasturba Medical College, Manipal 576104, India – sequence: 8 givenname: A. Krishna surname: Rao fullname: Rao, A. Krishna organization: Department of Ophthalmology, Kasturba Medical College, Manipal 576104, India – sequence: 9 givenname: Sobha surname: Sivaprasad fullname: Sivaprasad, Sobha organization: Consultant ophthalmologist, NIHR Moorfields Biomedical Research Centre, London, United Kingdom – sequence: 10 givenname: Kuang Chua surname: Chua fullname: Chua, Kuang Chua organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489 Singapore, Singapore – sequence: 11 givenname: Augustinus surname: Laude fullname: Laude, Augustinus organization: National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore – sequence: 12 givenname: Louis surname: Tong fullname: Tong, Louis organization: Singapore Eye Research Institute, Ocular Surface Research Group, Singapore, Singapore |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28351716$$D View this record in MEDLINE/PubMed |
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| Keywords | Glaucoma Diabetic retinopathy Fundus Age-related macular degeneration Continuous wavelet transform |
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| SubjectTerms | Accuracy Age Age-related macular degeneration Alcoholism Algorithms Amplitude modulation Automation Bleeding Blindness Blood vessels Classification Computation Computer applications Congestive heart failure Continuous wavelet transform Data processing Databases, Factual Decomposition Diabetes Diabetic retinopathy Diagnosis Diagnostic Techniques, Ophthalmological Digital computers Drug abuse Electroencephalography Entropy Epilepsy Evaluation Eye diseases Frequency dependence Frequency modulation Fundus Fundus Oculi Genetic transformation Glaucoma Glaucoma - diagnostic imaging Heart Humans Identification Image Interpretation, Computer-Assisted - methods Inspection Intelligence Internal Medicine International conferences Learning algorithms Machine learning Macular degeneration Medical diagnosis Medical imaging Morphology Neural networks Other Particle swarm optimization Photography Probability theory Quality Radiography Retina Retina - diagnostic imaging Retinal Diseases - diagnostic imaging Risk Technology utilization Ultrasound Vision Visual perception Warning Wavelet Analysis Wavelet transforms |
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| Title | Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies |
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