Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies
| Autor: | Louis Tong, Sulatha V. Bhandary, S. Vinitha Sree, U. Raghavendra, Jen Hong Tan, A. Krishna Rao, Yuki Hagiwara, Joel E.W. Koh, U. Rajendra Acharya, Sobha Sivaprasad, Kuang Chua Chua, Augustinus Laude |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
genetic structures
020205 medical informatics Databases Factual Fundus Oculi Entropy Wavelet Analysis Glaucoma Health Informatics 02 engineering and technology Diagnostic Techniques Ophthalmological Cross-validation Retina Retinal Diseases Image Interpretation Computer-Assisted 0202 electrical engineering electronic engineering information engineering medicine Humans Computer vision Continuous wavelet transform Mass screening business.industry Wavelet transform Diabetic retinopathy medicine.disease eye diseases Computer Science Applications Random forest Computer-aided diagnosis 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | Computers in biology and medicine. 84 |
| ISSN: | 1879-0534 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect