ACHIKO-M Database for high myopia analysis and its evaluation

Myopia is the leading public health concern with high prevalence in developed countries. In this paper, we present the ACHIKO-M fundus image database with both myopic and emmetropic cases for high myopia study. The database contains 705 myopic subjects and 151 normal subjects with both left eye and right eye images for each subject. In addition, various clinical data is also available, allowing correlation study of different risk factors. We evaluated two state-of-the-art automated myopia detection algorithms on this database to show how it can be used. Both methods achieve more than 90% accuracy for myopia diagnosis. We will also discuss how ACHIKO-M can be a good database for both scientific and clinical research of myopia

The global response: How cities and provinces around the globe tackled Covid-19 outbreaks in 2021

Background: Tackling the spread of COVID-19 remains a crucial part of ending the pandemic. Its highly contagious nature and constant evolution coupled with a relative lack of immunity make the virus difficult to control. For this, various strategies have been proposed and adopted including limiting contact, social isolation, vaccination, contact tracing, etc. However, given the heterogeneity in the enforcement of these strategies and constant fluctuations in the strictness levels of these strategies, it becomes challenging to assess the true impact of these strategies in controlling the spread of COVID-19.Methods: In the present study, we evaluated various transmission control measures that were imposed in 10 global urban cities and provinces in 2021 Bangkok, Gauteng, Ho Chi Minh City, Jakarta, London, Manila City, New Delhi, New York City, Singapore, and Tokyo.Findings: Based on our analysis, we herein propose the population-level Swiss cheese model for the failures and pit-falls in various strategies that each of these cities and provinces had. Furthermore, whilst all the evaluated cities and provinces took a different personalized approach to managing the pandemic, what remained common was dynamic enforcement and monitoring of breaches of each barrier of protection. The measures taken to reinforce the barriers were adjusted continuously based on the evolving epidemiological situation.Interpretation: How an individual city or province handled the pandemic profoundly affected and determined how the entire country handled the pandemic since the chain of transmission needs to be broken at the very grassroot level to achieve nationwide control

Medical imaging algorithm research for diagnosis of ocular diseases

Color retinal fundus images provide visual documentation of the health of a person's retina. With the widespread adoption of higher quality medical imaging techniques and data, there are increasing demands for medical image-based computer-aided diagnosis (CAD) systems to manage large volumes of data, provide objective assessments for decision support and help in labour-intensive observer-driven tasks. This thesis focuses on the development of 2-dimensional color retinal image analysis algorithms for automated optic cup localization in glaucoma, the leading cause of irreversible blindness worldwide. Traditionally, the optic cup is automatically segmented using image processing-based methods, often with many hand-crafted heuristics. With the incorporation of learning based techniques, the accuracy of medical image-based CAD systems has improved significantly and are now widely accepted and adopted by medical practitioners. In this dissertation, three novel approaches for automatic localization of the optic cup in retinal fundus images are presented. In the first work, a boundary-based cup detection approach using vessel kinks is presented. The key contribution in this work is its close modeling relationship with the clinical grading protocol to identify the optic cup, providing explicit visual evidence. Experimental results demonstrated that the novel use of vessel kinks as cup boundary key points guidance provides improved accuracy performance over existing retinal image processing based strategies. Although the use of vessel kinks is highly desirable and provides additional visual evidence, accurate detection and interpretation of these small vessel bends can, at times, be challenging. Instead, in the second work, a novel region-based unsupervised learning approach for automatic optic cup localization is proposed. This approach requires no training procedure, and utilizes domain knowledge and region-based features in a similarity-based label propagation and refinement scheme to obtain an estimated cup region. The promising result suggests that learning-based techniques are capable of accurate automatic optic cup localization. Recently, supervised superpixel-based cup localization has demonstrated superior performance. In the third work, a study on the limitations of this state-of-the-art classification framework is presented and an alternative generalized multi-scale approach is proposed, with improved stability and performance. This approach offers a stable and robust solution to reduce classification performance variations due to repeated random sampling of training samples. Furthermore, it integrates and unifies multiple superpixel resolutions for better boundary adherence. Extensive experimental results demonstrates the improved robustness and accuracy in optic cup localization against existing methods. In summary, three approaches for optic cup localization are proposed. This thesis demonstrate that using vessel kinks as cup margin key points is highly desirable and provides additional visual evidence. The challenges in the detection of these key points are also discussed. Alternatively, a region-based unsupervised learning approach is presented. Experimentally, it was shown that in the absence of ground-truth labels, this approach is able to achieve higher or comparable accuracy to the boundary-based and existing retinal image processing-based approaches. Lastly, the limitations of the state-of-the-art supervised superpixel-based cup localization approach are studied and improved with a novel multi-scale multi-model framework, which offers stability and improved accuracy.DOCTOR OF PHILOSOPHY (SCE

Method and system for determining the position of an optic cup boundary

US8428322Granted Paten

Automatic localization of retinal landmarks

Retinal landmark detection is a key step in retinal screening and computer-aided diagnosis for different types of eye diseases, such as glaucomma, age-related macular degeneration(AMD) and diabetic retinopathy. In this paper, we propose a semantic image transformation(SIT) approach for retinal representation and automatic landmark detection. The proposed SIT characterizes the local statistics of a fundus image and boosts the intrinsic retinal structures, such as optic disc(OD), macula. We propose our salient OD and macular models based on SIT for retinal landmark detection. Experiments on 5928 images show that our method achieves an accuracy of 99.44% in the detection of OD and an accuracy of 93.49% in the detection of macula, while having an accuracy of 97.33% for left and right eye classification. The proposed SIT can automatically detect the retinal landmarks and be useful for further eye-disease screening and diagnosis

Automatic glaucoma diagnosis through medical imaging informatics

Background - Computer-aided diagnosis for screening utilizes computer-based analytical methodologies to process patient information. Glaucoma is the leading irreversible cause of blindness. Due to the lack of an effective and standard screening practice, more than 50% of the cases are undiagnosed, which prevents the early treatment of the disease. Objective - To design an automatic glaucoma diagnosis architecture automatic glaucoma diagnosis through medical imaging informatics (AGLAIA-MII) that combines patient personal data, medical retinal fundus image, and patient's genome information for screening. Materials and methods - 2258 cases from a population study were used to evaluate the screening software. These cases were attributed with patient personal data, retinal images and quality controlled genome data. Utilizing the multiple kernel learning-based classifier, AGLAIA-MII, combined patient personal data, major image features, and important genome single nucleotide polymorphism (SNP) features. Results and discussion - Receiver operating characteristic curves were plotted to compare AGLAIA-MII's performance with classifiers using patient personal data, images, and genome SNP separately. AGLAIA-MII was able to achieve an area under curve value of 0.866, better than 0.551, 0.722 and 0.810 by the individual personal data, image and genome information components, respectively. AGLAIA-MII also demonstrated a substantial improvement over the current glaucoma screening approach based on intraocular pressure. Conclusions - AGLAIA-MII demonstrates for the first time the capability of integrating patients' personal data, medical retinal image and genome information for automatic glaucoma diagnosis and screening in a large dataset from a population study. It paves the way for a holistic approach for automatic objective glaucoma diagnosis and screening.ASTAR (Agency for Sci., Tech. and Research, S’pore

Risk perception and impact of severe acute respiratory syndrome (SARS) on work and personal lives of healthcare workers in Singapore: What can we learn?

10.1097/01.mlr.0000167181.36730.ccMedical Care437676-682MDLC