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P. Russo (2017)
Handbook of X-ray Imaging : Physics and Technology
Y Nakajima, K Yamada, K Imamura (2008)
Radiologist supply and workload: international comparison--working Group of Japanese College of RadiologyRadiat Med, 26
G. Nuttall (2007)
Hemostasis and Thrombosis: Basic Principles and Clinical Practice, 5th ed.Anesthesia & Analgesia, 104
(2001)
Tipos de trauma – o politraumatizado
R. Auer, J. Riehl (2017)
The incidence of deep vein thrombosis and pulmonary embolism after fracture of the tibia: An analysis of the National Trauma Databank.Journal of clinical orthopaedics and trauma, 8 1
(2003)
Sistemas Inteligentes: Fundamentos e Aplicações
Judith Andersen (1983)
Hemostasis and Thrombosis—Basic Principles and Clinical PracticeAnnals of Surgery, 197
P. Kamencay, M. Zachariasova, R. Hudec, M. Benco, R. Radil (2014)
3D image reconstruction from 2D CT slices2014 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON)
(2011)
Avariational approach to bone segmentation in CT images
G. Schmid, S. Lippmann, S. Unverzagt, C. Hofmann, T. Deutsch, T. Frese (2017)
The Investigation of Suspected Fracture-a Comparison of Ultrasound With Conventional Imaging.Deutsches Arzteblatt international, 114 45
W. Shadid, A. Willis (2018)
Bone fragment segmentation from 3D CT imageryComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 66
G. Moneta (2010)
Projected Cancer Risks From Computed Tomographic Scans Performed in the United States in 2007Yearbook of Vascular Surgery, 2010
(2001)
Trauma: a doença dos séculos. São Paulo: Atheneu
Yuxuan Huang, Zhongpan Qiu, Zhijun Song (2011)
3D reconstruction and visualization from 2D CT images2011 IEEE International Symposium on IT in Medicine and Education, 2
(2018)
SUS gasta R$ 4 milhões para atender acidentados em um ano
Marcelo Freitas (2005)
Panorama das exposições médicas em radiologia convencional no Estado de São Paulo
R. Shyamasundar (1996)
Introduction to algorithmsResonance, 1
N. Wirth (1976)
Algorithms + Data Structures = Programs
Y. Nakajima, Kei Yamada, K. Imamura, Kazuko Kobayashi (2008)
Radiologist supply and workload: international comparisonRadiation Medicine, 26
Frank Zeeuw (2016)
Graph Theory
S. Armato, Geoffrey Mclennan, Luc Bidaut, M. McNitt-Gray, C. Meyer, Anthony Reeves, Binsheng Zhao, Denise Aberle, C. Henschke, Eric Hoffman, Ella Kazerooni, H. MacMahon, Edwin Beek, David Yankelevitz, Alberto Biancardi, P. Bland, Matthew Brown, R. Engelmann, Gary Laderach, Daniel Max, Richard Pais, David Qing, R. Roberts, Amanda Smith, A. Starkey, Poonam Batra, P. Caligiuri, Ali Farooqi, G. Gladish, C. Jude, R. Munden, I. Petkovska, L Quint, Lawrence Schwartz, B. Sundaram, Lori Dodd, C. Fenimore, David Gur, Nicholas Petrick, J. Freymann, Justin Kirby, Brian Hughes, Alessi Casteele, Sangeeta Gupte, Maha Sallam, Michael Heath, Michael Kuhn, E. Dharaiya, Richard Burns, David Fryd, M. Salganicoff, V. Anand, U. Shreter, S. Vastagh, Barbara Croft, Laurence Clarke (2011)
The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.Medical physics, 38 2
P. Allisy-Roberts, Jerry Williams (2007)
Farr's Physics for Medical Imaging
J. Udupa, V. LeBlanc, Y. Zhuge, C. Imielinska, H. Schmidt, L. Currie, B. Hirsch, James Woodburn (2006)
A framework for evaluating image segmentation algorithmsComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 30 2
BA González, M Mahesh, KP Kim, M Bhargavan, R Lewis, F Mettler, C Land (2007)
Projected cancer risks from computed tomographic scans performed in the United States in 2007Arch Intern Med, 169
L. Dice (1945)
Measures of the Amount of Ecologic Association Between SpeciesEcology, 26
S. Haykin (2010)
Neural Networks and Learning Machines
Florence Loi, L. Córdova, J. Pajarinen, Tzuhua Lin, Z. Yao, S. Goodman (2016)
Inflammation, fracture and bone repair.Bone, 86
GL Schmid, S Lippmann, S Unverzagt, C Hofmann, T Deutsch, T Frese (2017)
The investigation of suspected fracture - a comparison of ultrasound with conventional imagingDtsch Arztebl Int, 114
Bo Zhao, H. Ding, Yang Lu, Ge Wang, Jun Zhao, S. Molloi (2012)
Dual-dictionary learning-based iterative image reconstruction for spectral computed tomography applicationPhysics in Medicine and Biology, 57
M. Gardner, S. Dorling (1998)
Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciencesAtmospheric Environment, 32
J. Verhaagen, JW McDonald (2013)
Handbook of clinical neurologySpinal Cord, 51
M. Prokop (2002)
[Radiation dose and image quality in computed tomography].RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin, 174 5
(2016)
3 - D visualization techniques for medical images : a comprehensive study
M. Kupinski, M. Giger (1998)
Automated seeded lesion segmentation on digital mammogramsIEEE Transactions on Medical Imaging, 17
Purpose More than 70 million computed tomography scans are made per year. A great number of them aim at the thoraxic region, due to the number of organs and structures within it. The 3D visualization of these structures, including the bone, can lead to a more precise medical diagnosis. There are a number of works regarding 3D bone reconstruction, but most fail to present a quantitative evaluation of their assessment or have not achieved an assessment close to 100%. We present an automatic method of bone segmentation followed by 3D reconstruction that approaches these current limitations. Methods The proposed methodology has three blocks: (1) Preprocessing, whereby a median filter was applied to images that presented a high level of noise; (2) feature extraction procedure, in which (i) the images intensity levels were converted to attenuation coefficients and (ii) a (MLP) neural network was used to populate the Space of Attributes with the corresponding feature vectors; and (3) 3D structural construction, whereby a red-and-black tree with graph guidance combined the regarding clustered feature vectors with their spatial neighbors. To evaluate the results, the accuracy between the 2D-segmented images and their corresponding gold standards was calculated. Results The material is composed
Research on Biomedical Engineering – Springer Journals
Published: Mar 12, 2019
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