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Glaucoma: HELP
Articles by Yuki Hagiwara
Based on 4 articles published since 2008
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Between 2008 and 2019, Yuki Hagiwara wrote the following 4 articles about Glaucoma.
 
+ Citations + Abstracts
1 Review Computer-aided diagnosis of glaucoma using fundus images: A review. 2018

Hagiwara, Yuki / Koh, Joel En Wei / Tan, Jen Hong / Bhandary, Sulatha V / Laude, Augustinus / Ciaccio, Edward J / Tong, Louis / Acharya, U Rajendra. ·Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore. · National University of Singapore, Institute of System Science. · Department of Ophthalmology, Kasturba Medical College, Manipal, India. · National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore. · Department of Medicine, Columbia University, New York, USA. · 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. · 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. Electronic address: aru@np.edu.sg. ·Comput Methods Programs Biomed · Pubmed #30337064.

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.

2 Article Automated retinal health diagnosis using pyramid histogram of visual words and Fisher vector techniques. 2018

Koh, Joel E W / Ng, Eddie Y K / Bhandary, Sulatha V / Hagiwara, Yuki / Laude, Augustinus / Acharya, U Rajendra. ·Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore. Electronic address: falco_peregrinus14@yahoo.co.uk. · School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore. · Department of Ophthalmology, Kasturba Medical College, Manipal, India. · Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore. · National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore. · 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. ·Comput Biol Med · Pubmed #29227822.

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.

3 Article Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies. 2017

Koh, Joel E W / Acharya, U Rajendra / Hagiwara, Yuki / Raghavendra, U / Tan, Jen Hong / Sree, S Vinitha / Bhandary, Sulatha V / Rao, A Krishna / Sivaprasad, Sobha / Chua, Kuang Chua / Laude, Augustinus / Tong, Louis. ·Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489 Singapore, Singapore. · Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489 Singapore, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, 599491 Singapore, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Malaysia, Malaysia. Electronic address: aru@np.edu.sg. · Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal University, Manipal 576104, India. · Visiting Scientist, Global Biomedical Technologies, CA, USA. · Department of Ophthalmology, Kasturba Medical College, Manipal 576104, India. · Consultant ophthalmologist, NIHR Moorfields Biomedical Research Centre, London, United Kingdom. · National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore. · Singapore Eye Research Institute, Ocular Surface Research Group, Singapore, Singapore; Singapore National Eye Center, Cornea and External Eye Disease Department, Singapore, Singapore; Duke-National University of Singapore Graduate Medical School, Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. ·Comput Biol Med · Pubmed #28351716.

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.

4 Article Automated screening system for retinal health using bi-dimensional empirical mode decomposition and integrated index. 2016

Acharya, U Rajendra / Mookiah, Muthu Rama Krishnan / Koh, Joel E W / Tan, Jen Hong / Bhandary, Sulatha V / Rao, A Krishna / Fujita, Hamido / Hagiwara, Yuki / Chua, Chua Kuang / Laude, Augustinus. ·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. · Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore. Electronic address: mkm2@np.edu.sg. · Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore. · Department of Ophthalmology, Kasturba Medical College, Manipal, 576104, India. · Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate, 020-0693, Japan. · National Healthcare Group Eye Institute, Tan Tock Seng Hospital, 308433, Singapore. ·Comput Biol Med · Pubmed #27253617.

ABSTRACT: Posterior Segment Eye Diseases (PSED) namely Diabetic Retinopathy (DR), glaucoma and Age-related Macular Degeneration (AMD) are the prime causes of vision loss globally. Vision loss can be prevented, if these diseases are detected at an early stage. Structural abnormalities such as changes in cup-to-disc ratio, Hard Exudates (HE), drusen, Microaneurysms (MA), Cotton Wool Spots (CWS), Haemorrhages (HA), Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in PSED can be identified by manual examination of fundus images by clinicians. However, manual screening is labour-intensive, tiresome and time consuming. Hence, there is a need to automate the eye screening. In this work Bi-dimensional Empirical Mode Decomposition (BEMD) technique is used to decompose fundus images into 2D Intrinsic Mode Functions (IMFs) to capture variations in the pixels due to morphological changes. Further, various entropy namely Renyi, Fuzzy, Shannon, Vajda, Kapur and Yager and energy features are extracted from IMFs. These extracted features are ranked using Chernoff Bound and Bhattacharyya Distance (CBBD), Kullback-Leibler Divergence (KLD), Fuzzy-minimum Redundancy Maximum Relevance (FmRMR), Wilcoxon, Receiver Operating Characteristics Curve (ROC) and t-test methods. Further, these ranked features are fed to Support Vector Machine (SVM) classifier to classify normal and abnormal (DR, AMD and glaucoma) classes. The performance of the proposed eye screening system is evaluated using 800 (Normal=400 and Abnormal=400) digital fundus images and 10-fold cross validation method. Our proposed system automatically identifies normal and abnormal classes with an average accuracy of 88.63%, sensitivity of 86.25% and specificity of 91% using 17 optimal features ranked using CBBD and SVM-Radial Basis Function (RBF) classifier. Moreover, a novel Retinal Risk Index (RRI) is developed using two significant features to distinguish two classes using single number. Such a system helps to reduce eye screening time in polyclinics or community-based mass screening. They will refer the patients to main hospitals only if the diagnosis belong to the abnormal class. Hence, the main hospitals will not be unnecessarily crowded and doctors can devote their time for other urgent cases.