[Show abstract][Hide abstract] ABSTRACT: Cataract remains a leading cause for blindness worldwide. Cataract diagnosis via human grading is subjective and time-consuming. Several methods of automatic grading are currently available, but each of them suffers from some drawbacks. In this paper, a new approach for automatic detection based on texture and intensity analysis is proposed to address the problems of existing methods and improve the performance from three aspects, namely ROI detection, lens mask generation and opacity detection. In the detection method, image clipping and texture analysis are applied to overcome the over-detection problem for clear lens images and global thresholding is exploited to solve the under-detection problem for severe cataract images. The proposed method is tested on 725 retro-illumination lens images randomly selected from a database of a community study. Experiments show improved performance compared with the state-of-the-art method.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:5044-7.
[Show abstract][Hide abstract] ABSTRACT: Cataract is the leading cause of blindness worldwide. In this paper, registration of retroillumination image is investigated for detection of cortical and posterior subcapsular cataract (PSC). The pupil region is detected by local entropy filtering. The anterior image and posterior image are registered using maximum normalized cross-correlation. The registration of anterior and posterior image can be used to detect opacity in the future. The proposed algorithms were tested using clinical data and the experimental results indicate that the proposed method could facilitate automatic cataract diagnosis.
[Show abstract][Hide abstract] ABSTRACT: In clinical diagnosis, a grade indicating the severity of nuclear cataract is often manually assigned by a trained ophthalmologist to a patient after comparing the lens' opacity severity in his/her slit-lamp images with a set of standard photos. This grading scheme is often subjective and time-consuming. In this paper, a novel computer-aided diagnosis method via ranking is proposed to facilitate nuclear cataract grading following conventional clinical decision-making process. The grade of nuclear cataract in a slit-lamp image is predicted using its neighboring labeled images in a ranked image list, which is achieved using a learned ranking function. This ranking function is learned via direct optimization on a newly proposed approximation to a ranking evaluation measure. Our proposed method has been evaluated by a large dataset composed of 1000 different cases, which are collected from an ongoing clinical population-based study. Both experimental results and comparison with several existing methods demonstrate the benefit of grading via ranking by our proposed method.
IEEE transactions on medical imaging. 01/2011; 30(1):94-107.
[Show abstract][Hide abstract] ABSTRACT: Cataract is a leading cause of blindness worldwide. Computer-aided cataract detection is two-fold significant. Firstly, it will be helpful in mass screening. Secondly, it can be used as the preprocessing step for computer-aided grading. In this paper, the enhanced texture feature is proposed based on the graders' expertise of cataract and the characteristics of the retro-illumination lens images. The statistics of the enhanced texture feature is used to train the linear discriminant analysis to detect the cataract. The accuracy of 84.8% is achieved on a clinical database that contains 4545 pairs of images. It demonstrates that the proposed method is promising for mass screening and as the preprocessing step for computer-aided grading.
18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, September 11-14, 2011; 01/2011
[Show abstract][Hide abstract] ABSTRACT: To validate a new computer-aided diagnosis (CAD) imaging program for the assessment of nuclear lens opacity.
Slit-lamp lens photographs from the Singapore Malay Eye Study (SiMES) were graded using both the CAD imaging program and manual assessment method by a trained grader using the Wisconsin Cataract Grading System. Cataract was separately assessed clinically during the study using Lens Opacities Classification System III (LOCS III). The repeatability of CAD and Wisconsin grading methods were assessed using 160 paired images. The agreement between the CAD and Wisconsin grading methods, and the correlations of CAD with Wisconsin and LOCS III were assessed using the SiMES sample (5547 eyes from 2951 subjects).
In assessing the repeatability, the coefficient of variation (CoV) was 8.10% (95% confidence interval [CI], 7.21-8.99), and the intraclass correlation coefficient (ICC) was 0.96 (95% CI, 0.93-0.96) for the CAD method. There was high agreement between the CAD and Wisconsin methods, with a mean difference (CAD minus Wisconsin) of -0.02 (95% limit of agreement, -0.91 and 0.87) and an ICC of 0.81 (95% CI, 0.80-0.82). CAD parameters were also significantly correlated with LOCS III grading (all P < 0.001).
This new CAD imaging program assesses nuclear lens opacity with results comparable to the manual grading using the Wisconsin System. This study shows that an automated, precise, and quantitative assessment of nuclear cataract is possible.
[Show abstract][Hide abstract] ABSTRACT: Cataracts are the leading cause of blindness worldwide, and nuclear cataract is the most common form of cataract. An algorithm for automatic diagnosis of nuclear cataract is investigated in this paper. Nuclear cataract is graded according to the severity of opacity using slit lamp lens images. Anatomical structure in the lens image is detected using a modified active shape model. On the basis of the anatomical landmark, local features are extracted according to clinical grading protocol. Support vector machine regression is employed for grade prediction. This is the first time that the nucleus region can be detected automatically in slit lamp images. The system is validated using clinical images and clinical ground truth on >5000 images. The success rate of structure detection is 95% and the average grading difference is 0.36 on a 5.0 scale. The automatic diagnosis system can improve the grading objectivity and potentially be used in clinics and population studies to save the workload of ophthalmologists.
IEEE Transactions on Biomedical Engineering 08/2010; · 2.35 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Glaucoma is the second leading cause of permanent blindness worldwide. Glaucoma can be diagnosed through measurement of neuro-retinal optic cup-to-disc ratio (CDR). Correctly determining the optic disc region of interest (ROI) will produce a smaller initial image which takes much lesser time taken to process compared to the entire image. The earlier ROI localization in the ARGALI system used a grid based method. The new algorithm adds a preprocessing step before analyzing the image. This step significantly improves the performance of the ROI detection. A batch of 1564 retinal images from the Singapore Eye Research Centre was used to compare the performance of the two methods. From the results, the earlier and new algorithm detects the ROI correctly for 88% and 96% of the images respectively. The results indicate potential applicability of the method for automated and objective mass screening for early detection of glaucoma.
Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on; 07/2010
[Show abstract][Hide abstract] ABSTRACT: Glaucoma is a leading cause of blindness. The presence and extent of progression of glaucoma can be determined if the optic cup can be accurately segmented from retinal images. In this paper, we present a framework which improves the detection of the optic cup. First, a region of interest is obtained from the retinal fundus image, and a pallor-based preliminary cup contour estimate is determined. Patches are then extracted from the ROI along this contour. To improve the usability of the patches, adaptive methods are introduced to ensure the patches are within the optic disc and to minimize redundant information. The patches are then analyzed for vessels by an edge transform which generates pixel segments of likely vessel candidates. Wavelet, color and gradient information are used as input features for a SVM model to classify the candidates as vessel or non-vessel. Subsequently, a rigourous non-parametric method is adopted in which a bi-stage multi-resolution approach is used to probe and localize the location of kinks along the vessels. Finally, contenxtual information is used to fuse pallor and kink information to obtain an enhanced optic cup segmentation. Using a batch of 21 images obtained from the Singapore Eye Research Institute, the new method results in a 12.64% reduction in the average overlap error against a pallor only cup, indicating viable improvements in the segmentation and supporting the use of kinks for optic cup detection.
[Show abstract][Hide abstract] ABSTRACT: Untreated glaucoma leads to permanent damage of the optic nerve and resultant visual field loss, which can progress to blindness. As glaucoma often produces additional pathological cupping of the optic disc (OD), cupdisc- ratio is one measure that is widely used for glaucoma diagnosis. This paper presents an OD localization method that automatically segments the OD and so can be applied for the cup-disc-ratio based glaucoma diagnosis. The proposed OD segmentation method is based on the observations that the OD is normally much brighter and at the same time have a smoother texture characteristics compared with other regions within retinal images. Given a retinal image we first capture the ODs smooth texture characteristic by a contrast image that is constructed based on the local maximum and minimum pixel lightness within a small neighborhood window. The centre of the OD can then be determined according to the density of the candidate OD pixels that are detected by retinal image pixels of the lowest contrast. After that, an OD region is approximately determined by a pair of morphological operations and the OD boundary is finally determined by an ellipse that is fitted by the convex hull of the detected OD region. Experiments over 71 retinal images of different qualities show that the OD region overlapping reaches up to 90.37% according to the OD boundary ellipses determined by our proposed method and the one manually plotted by an ophthalmologist.
[Show abstract][Hide abstract] ABSTRACT: Retinal image analysis is used by clinicians to diagnose and identify, if any, pathologies present in a patient's eye. The developments and applications of computer-aided diagnosis (CAD) systems in medical imaging have been rapidly increasing over the years. In this paper, we propose a system to classify left and right eye retinal images automatically. This paper describes our two-pronged approach to classify left and right retinal images by using the position of the central retinal vessel within the optic disc, and by the location of the macula with respect to the optic nerve head. We present a framework to automatically identify the locations of the key anatomical structures of the eye- macula, optic disc, central retinal vessels within the optic disc and the ISNT regions. A SVM model for left and right eye retinal image classification is trained based on the features from the detection and segmentation. An advantage of this is that other image processing algorithms can be focused on regions where diseases or pathologies and more likely to occur, thereby increasing the efficiency and accuracy of the retinal CAD system/pathology detection. We have tested our system on 102 retinal images, consisting of 51 left and right images each and achieved and accuracy of 94.1176%. The high experimental accuracy and robustness of this system demonstrates that there is potential for this system to be integrated and applied with other retinal CAD system, such as ARGALI, for a priori information in automatic mass screening and diagnosis of retinal diseases.
[Show abstract][Hide abstract] ABSTRACT: A novel content-based medical image retrieval method with metric learning via rank correlation is proposed in this paper.
A new rank correlation measure is proposed to learn a metric encoding the pairwise similarity between images via direct optimization.
Our method has been evaluated with a large population-based dataset composed of 5000 slit-lamp images with different nuclear
cataract severities. Experimental results and statistical analysis demonstrate the superiority of our method over several
popular metric learning methods in content-based slit-lamp image retrieval.
Machine Learning in Medical Imaging, First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings; 01/2010
[Show abstract][Hide abstract] ABSTRACT: Cataract is the leading cause of blindness and posterior subcapsular cataract (PSC) leads to significant visual impairment. An automatic approach for detecting PSC opacity in retro-illumination images is investigated. The features employed include intensity, edge, size and spatial location. The system was tested using 441 images. The automatic detection was compared with the human expert. The sensitivity and specificity are 82.6% and 80% respectively. The preliminary research indicates it is feasible to apply automatic detection in the clinical screening of PSC in the future.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:5359-62.
[Show abstract][Hide abstract] ABSTRACT: Pathological myopia is the seventh leading cause of blindness. We introduce a framework based on PAMELA (PAthological Myopia dEtection through peripapilLary Atrophy) for the detection of pathological myopia from fundus images. The framework consists of a pre-processing stage which extracts a region of interest centered on the optic disc. Subsequently, three analysis modules focus on detecting specific visual indicators. The optic disc tilt ratio module gives a measure of the axial elongation of the eye through inference from the deformation of the optic disc. In the texturebased ROI assessment module, contextual knowledge is used to demarcate the ROI into four distinct, clinically-relevant zones in which information from an entropy transform of the ROI is analyzed and metrics generated. In particular, the preferential appearance of peripapillary atrophy (PPA) in the temporal zone compared to the nasal zone is utilized by calculating ratios of the metrics. The PPA detection module obtains an outer boundary through a level-set method, and subtracts this region against the optic disc boundary. Temporal and nasal zones are obtained from the remnants to generate associated hue and color values. The outputs of the three modules are used as in a SVM model to determine the presence of pathological myopia in a retinal fundus image. Using images from the Singapore Eye Research Institute, the proposed framework reported an optimized accuracy of 90% and a sensitivity and specificity of 0.85 and 0.95 respectively, indicating promise for the use of the proposed system as a screening tool for pathological myopia.
[Show abstract][Hide abstract] ABSTRACT: Myopia is a growing concern in many societies. In extremely high myopia, pathological myopia, which can cause visual loss, can occur. Pathological myopia is also accompanied by various visually perceivable symptoms on the retina, such as peripapillary atrophy. PAMELA is an automatic system for the detection of pathological myopia through the presence of peripapillary atrophy. In this paper, we describe two modules in the PAMELA system based on texture analysis and gray level analysis. A decision engine is then used to fuse the two individual results to obtain an overall analysis. From the results run on a sample batch of images from the Singapore Eye Research Institute, a sensitivity of 0.9 and a specificity of 0.94 with a total accuracy of up to 92.5% is obtained. The promising results indicate good potential for further development of PAMELA as a tool for mass screening for the detection of pathological myopia. This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.
Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on; 01/2010
[Show abstract][Hide abstract] ABSTRACT: Nuclear cataract is the most common type of age-related cataract and it is clinically diagnosed using slit-lamp images. Objective measurement of the features in slit-lamp image is investigated using a computerized software system. The correlation between the features and the nuclear cataract grades is analyzed. Experimental results show that intensity of sulcus, color in the lens and nucleus region, intensity of nucleus, and color in posterior reflex region are the key features for grading nuclear cataract. This study of feature analysis can benefit clinical cataract diagnosis and clinical research. The feature analysis can also be utilized to investigate the performance of different graders and be employed in training of new graders.
[Show abstract][Hide abstract] ABSTRACT: Glaucoma is the second leading cause of blindness worldwide. The risk of glaucoma can be determined by calculating the cup to disc ratio in retinal fundus images. To accurately detect the optic cup, kinks or bends in small and medium vessels are important indicators of the cup boundary. In this paper, we present a method of detecting such vessels, through the extraction of patches and generation of hybrid features in a SVM-based model. The segmentation results show good potential for the further development of this method.
[Show abstract][Hide abstract] ABSTRACT: This paper presents a photometric restoration technique that automatically corrects shading within retinal images taken with a fundus camera. The proposed technique is based on the observation that the background of retinal images usually shows flat reflectance variations due to its high similarity in color and texture. It estimates shading through an iterative polynomial interpolation procedure that first estimates a shading image through a horizontal interpolation process and then improves the shading estimation by a vertical interpolation process. Once the shading image is estimated, a reflectance image can accordingly be determined based on the luminance of the retina image under study. Experiments on 161 retinal images of different qualities show promising results.
Proceedings of the International Conference on Image Processing, ICIP 2009, 7-10 November 2009, Cairo, Egypt; 01/2009
[Show abstract][Hide abstract] ABSTRACT: The genome-wide association (GWA) study is the latest approach in the development of genetic studies and is renowned for its widespread success in identifying disease variants within the genome for various common diseases. It is a highly popular study amongst geneticists worldwide, evident from the numerous GWA studies conducted in laboratories all over the world. This paper introduces various GWA study designs currently recognized, including other aspects such as the software tools and its progress thus far. Especially, the paper reviews the genetic studies for glaucoma, an ocular disease which can lead to irreversible and permanent vision loss. Glaucomatous progression can be slowed or even halted if detected early; however, genetic information on glaucoma has not been well established yet. Therefore, by conducting a GWA study on glaucoma to find comprehensive associated genetic variants, the early detection of glaucoma through GWA may finally be seen as a possibility.