Classification of pathology in diabetic eye disease

Proliferative diabetic retinopathy is a complication of diabetes that can eventually lead to blindness. Early identification of this complication reduces the risk of blindness by initiating timely treatment. We report the utility of pattern analysis tools linked with a simple linear discriminant analysis that not only identifies new vessel growth in the retinal fundus but also localises the area of pathology. Ten fluorescein images were analysed using seven feature descriptors including area, perimeter, circularity, curvature, entropy, wavelet second moment and the correlation dimension. Our results indicate that traditional features such as area or perimeter measures of neovascularisation associated with proliferative retinopathy were not sensitive enough to detect early proliferative retinopathy (SNR = 0.76, 0.75 respectively). The wavelet second moment provided the best discrimination with a SNR of 1.17. Combining second moment, curvature and global correlation dimension provided a 100% discrimination (SNR = 1)

Comparison of various methods to delineate blood vessels in retinal images

The blood vessels in the human retina are easily visualisable via digital fundus photography and provide an excellent window to the health of a patient affected by diseases of blood circulation such as diabetes. Diabetic retinopathy is identifiable through lesions of the vessels such as narrowing of the arteriole walls, beading of venules into sausage like structures and new vessel growth as an attempt to reperfuse ischaemic regions. Automated quantification of these lesions would be beneficial to diabetes research and to clinical practice, particularly for eye-screening programmes for the detection of eye-disease amongst diabetic persons

Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest

This paper is devoted to empirical investigation of novel multi-level ensemble meta classifiers for the detection and monitoring of progression of cardiac autonomic neuropathy, CAN, in diabetes patients. Our experiments relied on an extensive database and concentrated on ensembles of ensembles, or multi-level meta classifiers, for the classification of cardiac autonomic neuropathy progression. First, we carried out a thorough investigation comparing the performance of various base classifiers for several known sets of the most essential features in this database and determined that Random Forest significantly and consistently outperforms all other base classifiers in this new application. Second, we used feature selection and ranking implemented in Random Forest. It was able to identify a new set of features, which has turned out better than all other sets considered for this large and well-known database previously. Random Forest remained the very best classier for the new set of features too. Third, we investigated meta classifiers and new multi-level meta classifiers based on Random Forest, which have improved its performance. The results obtained show that novel multi-level meta classifiers achieved further improvement and obtained new outcomes that are significantly better compared with the outcomes published in the literature previously for cardiac autonomic neuropathy

THE MANUFACTURE OF SUPPLEMENTAL DEPLETED FUEL RODS FOR FCF STARTUP

Approximately 2000 supplemental rods were made for use in EBR-II Fuel Cycle Facility startup tests. They were made in the same manner as Core-I fuel rods but using partially depleted pins instead of fuel pins. A duplex'' or double melting operation was used for Core-I production. The alloys were first melted together and cast in and ingot mold. The ingot was then remelted and injection castto produce fuel pins. In order to simplify the operation, a single melt, or simplex'' operation, alloying and injecting casting in one step was tried. This operation was unsatisfactory because of uncontrollable gas evolution from the ingredients of the charge. The interior parts of the furnace became coated with condensed metal to an extent that threatened mechanical and electrical failure of the furnace. A thermocouple head was developed for use in the injection casting furnace. It had increased accuracy and reliability, and was more easily remotely replaced. The improvements were due to unit construction and improved cold-junction contacts. A statistical analysis was made of a sample of 412 rods. The analysis produced (1) and equation for predicting sodium levels through the selection of sodium loads, and (2) evidence that jacket-preassembly classification is necessary under existing specifications for sodium level. (auth

A survey of state-of-the-art methods for securing medical databases

This review article presents a survey of recent work devoted to advanced state-of-the-art methods for securing of medical databases. We concentrate on three main directions, which have received attention recently: attribute-based encryption for enabling secure access to confidential medical databases distributed among several data centers; homomorphic encryption for providing answers to confidential queries in a secure manner; and privacy-preserving data mining used to analyze data stored in medical databases for verifying hypotheses and discovering trends. Only the most recent and significant work has been included

Empirical investigation of multi-tier ensembles for the detection of cardiac autonomic neuropathy using subsets of the Ewing Features

This article is devoted to an empirical investigation of per- formance of several new large multi-tier ensembles for the detection of cardiac autonomic neuropathy (CAN) in diabetes patients using subsets of the Ewing features. We used new data collected by the diabetes screening research initiative (DiScRi) project, which is more than ten times larger than the data set originally used by Ewing in the investigation of CAN. The results show that new multi-tier ensembles achieved better performance compared with the outcomes published in the literature previously. The best accuracy 97.74% of the detection of CAN has been achieved by the novel multi-tier combination of AdaBoost and Bagging, where AdaBoost is used at the top tier and Bagging is used at the middle tier, for the set consisting of the following four Ewing features: the deep breathing heart rate change, the Valsalva manoeuvre heart rate change, the hand grip blood pressure change and the lying to standing blood pressure change

Retinal Image Quality Analysis For Automatic Diabetic Retinopathy Detection

Sufficient image quality is a necessary prerequisite for reliable automatic detection systems in several healthcare environments. Specifically for Diabetic Retinopathy (DR) detection, poor quality fund us makes more difficult the analysis of discontinuities that characterize lesions, as well as to generate evidence that can incorrectly diagnose the presence of anomalies. Several methods have been applied for classification of image quality and recently, have shown satisfactory results. However, most of the authors have focused only on the visibility of blood vessels through detection of blurring. Furthermore, these studies frequently only used fund us images from specific cameras which are not validated on datasets obtained from different retinographers. In this paper, we propose an approach to verify essential requirements of retinal image quality for DR screening: field definition and blur detection. The methods were developed and validated on two large, representative datasets collected by different cameras. The first dataset comprises 5,776 images and the second, 920 images. For field definition, the method yields a performance close to optimal with an area under the Receiver Operating Characteristic curve (ROC) of 96.0%. For blur detection, the method achieves an area under the ROC curve of 95.5%. © 2012 IEEE.229236Saaddine, J., Honeycutt, A., Narayan, K., Zhang, X., Klein, R., Boyle, J., Projection of diabetic retinopathy and other major eye diseases among people with diabetes mellitus: United states, 2005-2050 (2008) Arch Ophthalmol., 126 (12), pp. 1740-1747Spurling, G., Askew, D., Hansar, N.H.N., Cooney, A., Jackson, C., Retinal photography for diabetic retinopathy screening in indigenous primary health care: The inala experience (2010) Australian and New Zealand Journal of Public Health, 34, pp. S30-S33Pettitt, D.J., Wollitzer, A.O., Jovanovic, L., He, G., Ipp, E., Decreasing the risk of diabetic retinopathy in a study of case management: The California medi-cal type 2 diabetes study (2005) Diabetes Care, 28 (12), pp. 2819-2822. , http://care.diabetesjournals.org/cgi/reprint/28/12/2819, DOI 10.2337/diacare.28.12.2819Bragge, P., Gruen, R., Chau, M., Forbes, A., Taylor, H., Screening for presence or absence of diabetic retinopathy: A meta-analysis (2011) Arch Ophthalmol., 129 (4), pp. 435-444Maberley, D., Morris, A., Hay, D., Chang, A., Hall, L., Mandava, N., A comparison of digital retinal image quality among photographers with different levels of training using a non-mydriatic fundus camera (2004) Ophthalmic Epidemiology, 11 (3), pp. 191-197. , DOI 10.1080/09286580490514496Philip, S., Fleming, A.D., Goatman, K.A., Fonseca, S., Mcnamee, P., Scotland, G.S., Prescott, G.J., Olson, J.A., The efficacy of automated "disease/no disease" grading for diabetic retinopathy in a systematic screening programme (2007) British Journal of Ophthalmology, 91 (11), pp. 1512-1517. , DOI 10.1136/bjo.2007.119453Jelinek, H., Cree, M., (2010) Automated Image Detection of Retinal Pathology, , Boca Raton: CRC PressDavis, H., Russell, S., Barriga, E., Abramoff, M., Soliz, P., Visionbased, real-time retinal image quality assessment (2009) IEEE CMBS, pp. 1-6Giancardo, L., Meriaudeau, F., Karnowski, T., Chaum, E., Tobin, K., (2010) New Developments in Biomedical Engineering, pp. 201-224. , InTech, ch. Quality Assessment of Retinal Fundus Images using Elliptical Local Vessel DensityLalonde, M., Gagnon, L., Boucher, M.-C., Automatic visual quality assessment in optical fundus images (2001) Vision Interface, pp. 259-264Niemeijer, M., Abramoff, M.D., Van Ginneken, B., Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening (2006) Medical Image Analysis, 10 (6), pp. 888-898. , DOI 10.1016/j.media.2006.09.006, PII S1361841506000739Patton, N., Aslam, T.M., MacGillivray, T., Deary, I.J., Dhillon, B., Eikelboom, R.H., Yogesan, K., Constable, I.J., Retinal image analysis: Concepts, applications and potential (2006) Progress in Retinal and Eye Research, 25 (1), pp. 99-127. , DOI 10.1016/j.preteyeres.2005.07.001, PII S1350946205000406Jelinek, H., Rocha, A., Carvalho, T., Goldenstein, S., Wainer, J., Machine learning and pattern classification in identification of indigenous retinal pathology (2011) IEEE EMBSFacey, K., (2002) Health Tech. Assessment: Organisation of Services for Diabetic Retinopathy Screening, , Health Tech. Board for ScotlandFleming, A.D., Philip, S., Goatman, K.A., Olson, J.A., Sharp, P.F., Automated assessment of diabetic retinal image quality based on clarity and field definition (2006) Investigative Ophthalmology and Visual Science, 47 (3), pp. 1120-1125. , DOI 10.1167/iovs.05-1155Winn, J., Criminisi, A., Minka, T., Object categorization by learned universal visual dictionary (2005) Proceedings of the IEEE International Conference on Computer Vision, 2, pp. 1800-1807. , DOI 10.1109/ICCV.2005.171, 1544935, Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005Herbert, J., Pires, R., Padilha, R., Goldenstein, S., Wainer, J., Bossomaier, T., Rocha, A., Data fusion for multi-lesion diabetic retinopathy detection IEEE EMBS, 2012Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E., Image quality assessment: From error visibility to structural similarity (2004) IEEE Trans. on Image Processing, 13 (4), pp. 600-612Pizer Stephen, M., Amburn, E.P., Austin John, D., Cromartie, R., Geselowitz, A., Greer, T., Ter Haar Romeny, B., Zuiderveld, K., Adaptive histogram equalization and its variations (1987) Computer vision, graphics, and image processing, 39 (3), pp. 355-368Chang, C.-C., Lin, C.-J., LIBSVM: A library for support vector machines (2011) ACM Trans. on Intelligent Systems and Tech., 2, pp. 2701-2727Gonzalez, R., Woods, R., (2006) Digital Image Processing, , (3rd Ed.). Upper Saddle River, NJ, USA: Prentice-Hall, IncBay, H., Tuytelaars, T., Van Gool, L., SURF: Speeded up robust features (2006) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3951, pp. 404-417. , DOI 10.1007/11744023-32, Computer Vision - ECCV 2006, 9th European Conference on Computer Vision, ProceedingsSivic, J., Zisserman, A., Video google: A text retrieval approach to object matching in videos (2003) IEEE ICCV, pp. 1470-1477Do Valle Jr., E.A., (2008) Local-descriptor Matching for Image Identification Systems, , Ph.D. dissertation, Université de Cergy-Pontoise École Doctorale Sciences et Ingénierie, Cergy-Pontoise, France, JuneRocha, A., Papa, J., Meira, L., How far do we get using machine learning black-boxes? Intl. Journal of Pattern Recognition and Artificial Intelligence, 2012, pp. 1-

FABRICATION OF EBR-II, CORE-I FUEL PINS

A total of 11,117 enriched uranium-5 wt.% fissium alloy fuel pins was manufactured for EBR-II, Core I. These were made from a synthetic fission product alloy of nonradioactive elements, natural uranium, and enriched uranium. The material was supplied as precast billets. The manufacturing methods were developed for the EBR-III Fuel Cycle Facility. Experimental refabrication equipment was used to production test both the methods and the equipment. The billets were induction melted and pressure cast into precision-bore, high-silica glass molds in batches of 90 to 160. The number of molds used was adjusted according to batch weight. After casting, the molds were broken away and the castings were fed into a pin-process and inspection machine. Both ends were sheared from the castings to produce finished pins measuring 0.144 in. in diameter by 14.22 in. long. The pins were inspected for diameter, porosity, weight, and length. Rejected pins and sheared ends were broken into short lengths and returned for consolidation. Acceptable fuel pins were sealed and sodium bonded in stainless steel jackets, and assembled into Core-I fuel elements. (auth

Machine Learning And Pattern Classification In Identification Of Indigenous Retinal Pathology

Diabetic retinopathy (DR) is a complication of diabetes, which if untreated leads to blindness. DR early diagnosis and treatment improve outcomes. Automated assessment of single lesions associated with DR has been investigated for sometime. To improve on classification, especially across different ethnic groups, we present an approach using points-of-interest and visual dictionary that contains important features required to identify retinal pathology. Variation in images of the human retina with respect to differences in pigmentation and presence of diverse lesions can be analyzed without the necessity of preprocessing and utilizing different training sets to account for ethnic differences for instance. © 2011 IEEE.59515954Mitchell, P., Foran, S., Wong, T.Y., Chua, B., Patel, I., Ojaimi, E., (2008) Guidelines for the Management of Diabetic Retinopathy, , Canberra: NHMRCJelinek, H.F., Cornforth, D., Cree, M., Cesar R M, J., Leandro, J.J.G., Soares, J.V.B., Mitchell, P., Automated characterisation of diabetic retinopathy using mathematical morphology: A pilot study for community health (2003) NSW Primary Health Care Research and Evaluation Conference, p. 48. , SydneyCree, M.J., Olson, J.A., McHardy, K., Sharp, P., Forrester, J., A fully automated comparative microaneurysm digital detection system (1997) Eye, 11, pp. 622-628Karperien, A.L., Jelinek, H.F., Leandro, J.J.G., Soares, J.V.B., Cesar R M, J., Luckie, A., Automated detection of proliferative retinopathy in clinical practice (2008) Clinical Ophthalmology, 2, pp. 109-122Wang, H., Hsu, W., Goh, K.G., Lee, M.L., An effective approach to detect lesions in colour retinal images (2000) IEEE Int. Conf. in Computer Vision and Pattern Recognition, pp. 181-187Streeter, L., Cree, M.J., Microaneurysm detection in colour fundus images (2003) Image and Vision Computing, pp. 280-284Goatman, K.A., Cree, M.J., Olson, J.A., Sharp, P.F., Forrester, J.V., Automated measurement of microaneurysm turnover (2003) Investigative Ophthalmology and Visual Science, 44, pp. 5335-5341Cree, M.J., Gamble, E., Cornforth, D.J., Colour normalisation to reduce inter-patient and intra-patient variability in microaneurysm detection in colour retinal images (2005) Workshop on Digital Image Computing, pp. 163-169. , Brisbane, AustraliaValle, E., Cord, M., Philipp-Foliguet, S., High-dimensional descriptor indexing for large multimedia databases (2008) ACM Intl. Conf. on Information and Knowledge Management, pp. 739-748Bay, H., Tuytelaars, T., Gool, L.V., SURF: Speeded up robust features (2006) European Conf. on Computer Vision, pp. 1-14Viola, P., Jones, M., Robust real-time face detection (2004) Intl. Journa of Computer Vision, 52, pp. 137-154Rocha, A., Carvalho, T., Goldenstein, S., Wainer, J., (2011) Points of Interest and Visual Dictionary for Retina Pathology Detection, , Technical Report IC-11-07, Institute of Computing, Univ. of Campinas, Campinas, Brazi