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International Conference on Contemporary computing and Informatics (IC3I)

作者： P N Sharath Kumar R Rajesh Kumar Anuja Sathar V Sahasranamam Health Informatics and Software Technology Group Centre for Development of Advanced Computing Thiruvananthapuram India Department of Ophthalmology Regional Institute of Ophthalmology Thiruvananthapuram India

These days, Automated screening systems are becoming the best possible solution for not only reducing burden on the medical practitioners but also reducing human prone errors with the help of accurate automated diagnosis of the disease. In Diabetic Retinopathy (DR), the crucial step for an automated system is exhaustive detection of the lesions. In this paper, a modified red lesion detection method is presented based on some changes to the filters used in the prior work by Spencer et al. Along with this, a novel three stage false positive elimination technique is presented to remove non red-lesion candidates detected in the modified filter approach. Firstly, false positive candidates detected elsewhere in the retina are removed. Secondly, falsely found candidates on the blood vessels are removed. Third, candidates found in the optic disc area are removed using our earlier work. To evaluate our method for the detection of red lesions, we examined two sets of fundus images, first set constituting of 94 images obtained from the routine screening at Regional Institute of Ophthalmology, Thiruvananthapuram and the second set of 89 images from DIARETDB1 database. When determining whether an image contains red lesions, our method achieved a sensitivity of 95.6% and specificity of 93.2%. Also, our method detected 78.9% red lesions present in each of the images.

关键词： Lesions Biomedical imaging Retina Blood vessels Image color analysis Optical filters Optical imaging

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Automatic detection of exudates in retinal images using histogram analysis

Automatic detection of exudates in retinal images using hist...

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2013 IEEE Recent Advances in Intelligent Computational systems, RAICS 2013

作者： Sharath Kumar, P.N. Kumar, R Rajesh Sathar, Anuja Sahasranamam, V. Health Informatics and Software Technology Group Centre for Development of Advanced Computing Thiruvananthapuram India Department of Ophthalmology Regional Institute of Ophthalmology Thiruvananthapuram India

ISBN: (纸本)9781479921782

Diabetic Retinopathy (DR) is the major cause of blindness caused by the damage to the blood vessels in the retina from diabetes. It cannot be prevented but early detection through fundus imaging by an ophthalmologist can prevent further vision loss. Presence of microaneurysms, hemorrhages, cotton-wool spots and exudates are the symptoms of mild DR. Of these, the detection of exudates is one of the important factors in the early diagnosis of DR. Exudates are fatty deposits on the retina which appear as yellowish regions in fundus image. Fundus images show considerable variation in brightness which makes automatic detection of exudates difficult. In this study, we are proposing a new method for preprocessing and false positive elimination towards the reliable detection of exudates. The brightness of the fundus image was changed by the nonlinear curve with brightness values of the hue saturation value (HSV) space. To emphasize brighter yellow regions (exudates), gamma correction was performed on each red and green components of the image. Subsequently, the histograms of each red and green component were extended. After that, the exudates candidates were detected using histogram analysis. Finally, false positives were removed by using multi-channel histogram analysis. To evaluate the new method for the detection of exudates, we examined 158 fundus images, including 84 abnormal images with exudates and 74 normal images. The sensitivity and specificity for the detection of abnormal and normal cases were 88.45% and 95.5% respectively. © 2013 IEEE.

关键词： Blood vessels

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Templated marching cubes - A low computation approach to surface rendering

Templated marching cubes - A low computation approach to sur...

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2013 IEEE Recent Advances in Intelligent Computational systems, RAICS 2013

作者： Manikandtan, C.K. Resmi, S. Sibi, S. Kumar, R. Rajesh Harikumaran Nair, G.S. Health and Software Technology Group Centre for Development of Advanced Computing Thiruvananthapuram India Dept. of Radiodiagnosis Medical College Hospital Thiruvananthapuram India

ISBN: (纸本)9781479921782

Surface generation from high resolution datasets using triangulation algorithms like marching cubes require large amounts of computational time for the generation and interpolation of vertices. Here we propose a templated method of generating triangles which has far less computation involved and saves on CPU cycles and memory. Each cube orientation corresponding to the boundary cases in the original algorithm is listed in a prebuilt table of templated triangles. The template created using binary input may be further smoothened using cost functions related to input image data. © 2013 IEEE.

关键词： Geometry

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Templated marching cubes — A low computation approach to surface rendering

Templated marching cubes — A low computation approach to su...

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IEEE Recent Advances in Intelligent Computational systems (RAICS)

作者： C K Manikandtan S. Resmi S Sibi R. Rajesh Kumar G S Harikumaran Nair Health and Software Technology Group Centre for Development of Advanced Computing Thiruvananthapuram India Health and Software Technology Group Centre for Development of Advanced Computing Thiruvananthapuram India Medical College Hospital Arpookara Kerala IN

关键词： Isosurfaces Biomedical imaging Interpolation Computational modeling Data models Standards

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Automatic detection of exudates in retinal images using histogram analysis

Automatic detection of exudates in retinal images using hist...

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IEEE Recent Advances in Intelligent Computational systems (RAICS)

关键词： Optical imaging Optical filters Optical saturation Histograms Adaptive optics Image color analysis Diabetes

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Cloud computing for Internet of Things & sensing based applications

Cloud computing for Internet of Things & sensing based appli...

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2012 6th International Conference on Sensing Technology, ICST 2012

作者： Rao, B. B Prahlada Saluia, Paval Sharma, Neetu Mittal, Ankit Sharma, Shivay Veer Centre for Development of Advanced Computing System Software Development Group C-DAC Knowledge Park #1 Old Madras Road Bangalore-560038 India

ISBN: (纸本)9781467322485

Internet of Things (IoT) is a concept that envisions all objects around us as part of internet. IoT coverage is very wide and include variety of objects like smart phones, tablets, digital cameras, sensors, etc. Once all these devices are connected with each other, they enable more and more smart processes and services that support our basic needs, economies, environment and health. Such enormous number of devices connected to internet provides many kinds of services and produce huge amount of data and information. Cloud computing is a model for on-demand access to a shared pool of configurable resources (e.g. compute, networks, servers, storage, applications, services, and software) that can be easily provisioned as Infrastructure (IaaS), software and applications (SaaS). Cloud based platforms help to connect to the things (IaaS) around us so that we can access anything at any time and any place in a user friendly manner using customized portals and in built applications (SaaS). Hence, cloud acts as a front end to access Internet of Things. Applications that interact with devices like sensors have special requirements of massive storage to storage big data, huge computation power to enable the real time processing of the data, and high speed network to stream audio or video. In this paper, we describe how Internet of Things and Cloud computing can work together can address the Big Data issues. We also illustrate about Sensing as a service on cloud using few applications like Augmented Reality, Agriculture and Environment monitoring. Finally, we also propose a prototype model for providing sensing as a service on cloud. © 2012 IEEE.

关键词： Internet of things

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Cloud computing for Internet of Things & sensing based applications

Cloud computing for Internet of Things & sensing based appli...

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International Conference on Sensing Technology, ICST

作者： B. B Prahlada Rao Paval Saluia Neetu Sharma Ankit Mittal Shivay Veer Sharma Centre for Development of Advanced Computing Juhu Maharashtra IN Centre for Development of Advanced Computing System Software Development Group Bangalore INDIA

关键词： Sensors Cloud computing Clouds Agriculture Computational modeling Servers

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

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Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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