Literatura científica selecionada sobre o tema "Medical image sequences"
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Artigos de revistas sobre o assunto "Medical image sequences"
Sagerer, G. "Automatic interpretation of medical image sequences". Pattern Recognition Letters 8, n.º 2 (setembro de 1988): 87–102. http://dx.doi.org/10.1016/0167-8655(88)90050-5.
Texto completo da fonteWang, Xiaowei, Shoulin Yin, Muhammad Shafiq, Asif Ali Laghari, Shahid Karim, Omar Cheikhrouhou, Wajdi Alhakami e Habib Hamam. "A New V-Net Convolutional Neural Network Based on Four-Dimensional Hyperchaotic System for Medical Image Encryption". Security and Communication Networks 2022 (14 de fevereiro de 2022): 1–14. http://dx.doi.org/10.1155/2022/4260804.
Texto completo da fonteDing, Wei Li, Feng Jiang e Jia Qing Yan. "Automatic Segmentation of the Skull in MRI Sequences Using Level Set Method". Applied Mechanics and Materials 58-60 (junho de 2011): 2370–75. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2370.
Texto completo da fonteMalawski, Filip, e Lukasz Czekierda. "COMPRESSION OF IMAGE SEQUENCES IN INTERACTIVE MEDICAL TELECONSULTATIONS". Computer Science 18, n.º 1 (2017): 95. http://dx.doi.org/10.7494/csci.2017.18.1.95.
Texto completo da fonteYi, Fan, e Peihua Qiu. "Edge-Preserving Denoising of Image Sequences". Entropy 23, n.º 10 (12 de outubro de 2021): 1332. http://dx.doi.org/10.3390/e23101332.
Texto completo da fonteAn, Dezhi, Jun Lu, Shengcai Zhang, Yan Li e António M. Lopes. "A Novel Selective Encryption Method Based on Skin Lesion Detection". Mathematical Problems in Engineering 2020 (28 de setembro de 2020): 1–13. http://dx.doi.org/10.1155/2020/7982192.
Texto completo da fonteMa, Bin, Bing Li, Xiao-Yu Wang, Chun-Peng Wang, Jian Li e Yun-Qing Shi. "Code Division Multiplexing and Machine Learning Based Reversible Data Hiding Scheme for Medical Image". Security and Communication Networks 2019 (17 de janeiro de 2019): 1–9. http://dx.doi.org/10.1155/2019/4732632.
Texto completo da fonteWang, Yi Gang, Gang Yi Jiang e Mei Yu. "Study on Medical Micro-Image Mosaic with SIFT Features". Advanced Materials Research 121-122 (junho de 2010): 476–81. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.476.
Texto completo da fonteJiang, Huiyan, Hanqing Tan e Benqiang Yang. "A Priori Knowledge and Probability Density Based Segmentation Method for Medical CT Image Sequences". BioMed Research International 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/769751.
Texto completo da fonteHuang, Tongyuan, Jia Xu, Yuling Yang e Baoru Han. "Robust Zero-Watermarking Algorithm for Medical Images Using Double-Tree Complex Wavelet Transform and Hessenberg Decomposition". Mathematics 10, n.º 7 (2 de abril de 2022): 1154. http://dx.doi.org/10.3390/math10071154.
Texto completo da fonteTeses / dissertações sobre o assunto "Medical image sequences"
Fan, Li. "3D reconstruction and deformation analysis from medical image sequences with applications in left ventricle and lung /". free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9999280.
Texto completo da fonteForsberg, Anni. "Enhancement of X-ray Fluoroscopy Image Sequences using Temporal Recursive Filtering and Motion Compensation". Thesis, Linköping University, Department of Biomedical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-6494.
Texto completo da fonteThis thesis consider enhancement of X-ray fluoroscopy image sequences. The purpose is to investigate the possibilities to improve the image enhancement in Biplanar 500, a fluoroscopy system developed by Swemac Medical Appliances, for use in orthopedic surgery.
An algorithm based on recursive filtering, for temporal noise suppression, and motion compensation, for avoidance of motion artifacts, is developed and tested on image sequences from the system. The motion compensation is done both globally, by using the theory of the shift theorem, and locally, by subtracting consecutive frames. Also a new type of contrast adjustment is presented, received with an unlinear mapping function.
The result is a noise reduced image sequence that shows no blurring effects upon motion. A brief study of the result shows, that both the image sequences with this algorithm applied and the contrast adjusted images are preferred by orthopedists compared to the present images in the system.
Dietenbeck, Thomas. "Segmentation of 2D-echocardiographic sequences using level-set constrained with shape and motion priors". Phd thesis, INSA de Lyon, 2012. http://tel.archives-ouvertes.fr/tel-00838767.
Texto completo da fonteZhang, Heye. "An inverse framework for estimating cardiac electrophysiological activity from medical image sequence /". View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?ECED%202007%20ZHANGHY.
Texto completo da fonteSjölund, Jens. "MRI based radiotherapy planning and pulse sequence optimization". Licentiate thesis, Linköpings universitet, Medicinsk informatik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-115796.
Texto completo da fonteMhedhbi, Imen. "Compression en qualité diagnostic de séquences d’images médicales pour des plateformes embarquées". Electronic Thesis or Diss., Paris 6, 2015. http://www.theses.fr/2015PA066745.
Texto completo da fonteHospitals and medical centers produce an enormous amount of digital medical images every day especially in the form of image sequences. Due to the large storage size and limited transmission and width, an efficient compression technique is necessary. We first proposed a compressor algorithm for medical images sequences MMWaaves. It is based on Markov fields coupled with the certified medical device Waaves of Cira company. We demonstrated that MMWaaves provided a compression gains greater than 30% compared to JPEG2000 and Waaves while ensuring outstanding image quality for medical diagnosis (SSIM> 0.98). In addition, it achieved compression rates equal to those obtained by H.264 while improving the image quality. Then we developed a new compression algorithm MLPWaaves based on DWT difference followed by a new adaptive scanning model LPEAM in order to optimize the local stationary of wavelet coefficients. We obtained a compression gain up to 80% compared to Waaves and JPEG2000 while ensuring exceptional quality for medical diagnosis. Finally, in order to transmit medical images for diagnostic from the health center to the mobile device of the doctor, we proposed client-server remote radiology system for encoding and decoding. It is based on a multithreading paradigm to accelerate treatment. The validation of this solution was performed on two different platforms. We achieved an acceleration factor of 5 on an Intel Core i7-2600 and a factor of 3 on Samsung Galaxy tablet
Hernàndez, i. Sabaté Aura. "Exploring Arterial Dynamics and Structures in IntraVascular UltraSound Sequences". Doctoral thesis, Universitat Autònoma de Barcelona, 2009. http://hdl.handle.net/10803/5792.
Texto completo da fonteAquesta tesi proposa vàries eines de processament d'imatge per explorar la dinàmica de les artèries i les seves estructures. Presentem un model físic per extreure, analitzar i corregir la dinàmica rígida transversal dels vasos i per recuperar la fase cardíaca. A més, introduïm un mètode estadístic-determinista per a la detecció automàtica de les vores del vas. En particular, l'enfoquem a segmentar l'adventícia. Un protocol de validació acurat per assegurar una aplicació clínica fiable dels mètodes és un pas crucial en qualsevol proposta d'algorisme. En aquesta tesi tenim especial cura de dissenyar protocols de validació per a cadascuna de les tècniques proposades i contribuïmm a la validació de la dinàmica in vivo amb un indicador objectiu i quantitatiu per mesurar la quantitat de moviment suprimida.
Cardiovascular diseases are a leading cause of death in developed countries. Most of them are caused by arterial (specially coronary) diseases, mainly caused by plaque accumulation. Such pathology narrows blood flow (stenosis) and affects artery bio-mechanical elastic properties (atherosclerosis). In the last decades, IntraVascular UltraSound (IVUS) has become a usual imaging technique for the diagnosis and follow up of arterial diseases. IVUS is a catheter-based imaging technique which shows a sequence of cross sections of the artery under study. Inspection of a single image gives information about the percentage of stenosis. Meanwhile, inspection of longitudinal views provides information about artery bio-mechanical properties, which can prevent a fatal outcome of the cardiovascular disease. On one hand, dynamics of arteries (due to heart pumping among others) is a major artifact for exploring tissue bio-mechanical properties. On the other one, manual stenosis measurements require a manual tracing of vessel borders, which is a time-consuming task and might suffer from inter-observer variations.
This PhD thesis proposes several image processing tools for exploring vessel dynamics and structures. We present a physics-based model to extract, analyze and correct vessel in-plane rigid dynamics and to retrieve cardiac phase. Furthermore, we introduce a deterministic-statistical method for automatic vessel borders detection. In particular, we address adventitia layer segmentation. An accurate validation protocol to ensure reliable clinical applicability of the methods is a crucial step in any proposal of an algorithm. In this thesis we take special care in designing a validation protocol for each approach proposed and we contribute to the in vivo dynamics validation with a quantitative and objective score to measure the amount of motion suppressed.
Elbita, Abdulhakim M. "Efficient Processing of Corneal Confocal Microscopy Images. Development of a computer system for the pre-processing, feature extraction, classification, enhancement and registration of a sequence of corneal images". Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6463.
Texto completo da fonteThe data and image files accompanying this thesis are not available online.
Elbita, Abdulhakim Mehemed. "Efficient processing of corneal confocal microscopy images : development of a computer system for the pre-processing, feature extraction, classification, enhancement and registration of a sequence of corneal images". Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6463.
Texto completo da fonteGérard, Olivier. "Modelisation de sequences par techniques adaptatives : prevision de decharges de batterie et extraction de contours dans des images medicales". Paris 6, 1999. http://www.theses.fr/1999PA066565.
Texto completo da fonteLivros sobre o assunto "Medical image sequences"
L, Kamberova Gerda, e Shah Shishir Kirit 1971-, eds. DNA array image analysis: Nuts & bolts. Skippack, PA: DNA Press, 2002.
Encontre o texto completo da fonteIbrahim, El-Sayed H. Heart Mechanics: Magnetic Resonance Imaging--Mathematical Modeling, Pulse Sequences, and Image Analysis. Taylor & Francis Group, 2017.
Encontre o texto completo da fonteIbrahim, El-Sayed H. Heart Mechanics: Magnetic Resonance Imaging--Mathematical Modeling, Pulse Sequences, and Image Analysis. Taylor & Francis Group, 2017.
Encontre o texto completo da fonteIbrahim, El-Sayed H. Heart Mechanics: Magnetic Resonance Imaging--Mathematical Modeling, Pulse Sequences, and Image Analysis. Taylor & Francis Group, 2017.
Encontre o texto completo da fonteIbrahim, El-Sayed H. Heart Mechanics: Magnetic Resonance Imaging--Mathematical Modeling, Pulse Sequences, and Image Analysis. Taylor & Francis Group, 2017.
Encontre o texto completo da fonteIbrahim, El-Sayed H. Heart Mechanics: Magnetic Resonance Imaging - Mathematical Modeling, Pulse Sequences and Image Analysis. Taylor & Francis Group, 2017.
Encontre o texto completo da fonteWebb, Heather. Dante, Artist of Gesture. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192866998.001.0001.
Texto completo da fonteKamberova, Gerda, e Shishir Shah. DNA Array Image Analysis: Nuts & Bolts (Nuts & Bolts series). DNA Press, 2002.
Encontre o texto completo da fonteMitchell, Scott A. Buddhism in America. Bloomsbury Publishing Plc, 2016. http://dx.doi.org/10.5040/9781474204064.
Texto completo da fonteCapítulos de livros sobre o assunto "Medical image sequences"
Koprowski, Robert. "Analysis of Image Sequences". In Processing Medical Thermal Images, 83–119. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61340-6_6.
Texto completo da fonteRoos, Paul, e Max A. Viergever. "Reversible Data Compression of Angiographic Image Sequences". In Medical Images: Formation, Handling and Evaluation, 595–605. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77888-9_31.
Texto completo da fonteVicar, Tomas, Roman Jakubicek, Jiri Chmelik e Radim Kolar. "Registration of Medical Image Sequences Using Auto-differentiation". In Medical Imaging and Computer-Aided Diagnosis, 169–78. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-16-6775-6_15.
Texto completo da fonteKim, Wonjin, Wonkyeong Lee, Sun-Young Jeon, Nayeon Kang, Geonhui Jo e Jang-Hwan Choi. "Deep Denoising Network for X-Ray Fluoroscopic Image Sequences of Moving Objects". In Machine Learning for Medical Image Reconstruction, 95–104. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17247-2_10.
Texto completo da fonteAroukatos, Nikolaos G., Kostas Manes e Stelios Zimeras. "Social Networks Medical Image Steganography Using Sub-Fibonacci Sequences". In Annals of Information Systems, 171–85. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23341-3_13.
Texto completo da fonteAzzabou, Noura, e Nikos Paragios. "Spatio-temporal Speckle Reduction in Ultrasound Sequences". In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008, 951–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85988-8_113.
Texto completo da fonteLi, Wanyue, Yi He, Wen Kong, Jing Wang, Guohua Deng, Yiwei Chen e Guohua Shi. "SequenceGAN: Generating Fundus Fluorescence Angiography Sequences from Structure Fundus Image". In Simulation and Synthesis in Medical Imaging, 110–20. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87592-3_11.
Texto completo da fonteAlberti, Marina, Simone Balocco, Xavier Carrillo, Josepa Mauri e Petia Radeva. "Automatic Non-rigid Temporal Alignment of IVUS Sequences". In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, 642–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33415-3_79.
Texto completo da fonteSundar, Hari, Ali Khamene, Liron Yatziv e Chenyang Xu. "Automatic Image-Based Cardiac and Respiratory Cycle Synchronization and Gating of Image Sequences". In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, 381–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04271-3_47.
Texto completo da fonteSong, Xubo B., Andriy Myronenko, Stephen R. Plank e James T. Rosenbaum. "Registration of Microscopic Iris Image Sequences Using Probabilistic Mesh". In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006, 553–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11866763_68.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Medical image sequences"
Richens, Dominic, Morris Goldberg e Brian Morton. "Multimodality workstation for cardiac image sequences". In Medical Imaging 1993, editado por Yongmin Kim. SPIE, 1993. http://dx.doi.org/10.1117/12.146993.
Texto completo da fonteSkrinjar, Oskar, Yi-Yu Chou e Hemant Tagare. "Transitive nonrigid image registration: application to cardiac MR image sequences". In Medical Imaging 2004, editado por J. Michael Fitzpatrick e Milan Sonka. SPIE, 2004. http://dx.doi.org/10.1117/12.536230.
Texto completo da fonteDanudibroto, Adriyana, Jørn Bersvendsen, Oana Mirea, Olivier Gerard, Jan D'hooge e Eigil Samset. "Image-based temporal alignment of echocardiographic sequences". In SPIE Medical Imaging, editado por Neb Duric e Brecht Heyde. SPIE, 2016. http://dx.doi.org/10.1117/12.2216192.
Texto completo da fonteHartmann, M., M. A. Simons, B. Yih e R. A. Kruger. "Blood Flow Determination From Fluoroscopic Image Sequences". In Medical Imaging II, editado por Roger H. Schneider e Samuel J. Dwyer III. SPIE, 1988. http://dx.doi.org/10.1117/12.968658.
Texto completo da fonteFarison, James B., Yong-gab Park, Qun Yu e Hong Lu. "KL transformation of spatially invariant image sequences". In Medical Imaging 1997, editado por Kenneth M. Hanson. SPIE, 1997. http://dx.doi.org/10.1117/12.274108.
Texto completo da fonteHiggins, William E., Andrien J. Wang e Joseph M. Reinhardt. "Semiautomatic 4D analysis of cardiac image sequences". In Medical Imaging 1996, editado por Eric A. Hoffman. SPIE, 1996. http://dx.doi.org/10.1117/12.237878.
Texto completo da fonteEhrhardt, Jan, Dennis Säring e Heinz Handels. "Optical flow based interpolation of temporal image sequences". In Medical Imaging, editado por Joseph M. Reinhardt e Josien P. W. Pluim. SPIE, 2006. http://dx.doi.org/10.1117/12.652559.
Texto completo da fonteKennedy, Jonathon M., Michael Simms, Emma Kearney, Anita Dowling, Andrew Fagan e Neil J. O'Hare. "High-speed lossless compression for angiography image sequences". In Medical Imaging 2001, editado por Seong K. Mun. SPIE, 2001. http://dx.doi.org/10.1117/12.428103.
Texto completo da fonteClose, Robert A., James S. Whiting, Xiaolin Da e Neal L. Eigler. "Stabilized display of coronary x-ray image sequences". In Medical Imaging 2004, editado por Robert L. Galloway, Jr. SPIE, 2004. http://dx.doi.org/10.1117/12.535854.
Texto completo da fonteRoos, Paul, e Max A. Viergever. "Registration And Reversible Compression Of Angiographic Image Sequences". In 1989 Medical Imaging, editado por Samuel J. Dwyer III, R. Gilbert Jost e Roger H. Schneider. SPIE, 1989. http://dx.doi.org/10.1117/12.953279.
Texto completo da fonte