Auswahl der wissenschaftlichen Literatur zum Thema „Medical image sequences“
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Zeitschriftenartikel zum Thema "Medical image sequences"
Sagerer, G. „Automatic interpretation of medical image sequences“. Pattern Recognition Letters 8, Nr. 2 (September 1988): 87–102. http://dx.doi.org/10.1016/0167-8655(88)90050-5.
Der volle Inhalt der QuelleWang, Xiaowei, Shoulin Yin, Muhammad Shafiq, Asif Ali Laghari, Shahid Karim, Omar Cheikhrouhou, Wajdi Alhakami und Habib Hamam. „A New V-Net Convolutional Neural Network Based on Four-Dimensional Hyperchaotic System for Medical Image Encryption“. Security and Communication Networks 2022 (14.02.2022): 1–14. http://dx.doi.org/10.1155/2022/4260804.
Der volle Inhalt der QuelleDing, Wei Li, Feng Jiang und Jia Qing Yan. „Automatic Segmentation of the Skull in MRI Sequences Using Level Set Method“. Applied Mechanics and Materials 58-60 (Juni 2011): 2370–75. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2370.
Der volle Inhalt der QuelleMalawski, Filip, und Lukasz Czekierda. „COMPRESSION OF IMAGE SEQUENCES IN INTERACTIVE MEDICAL TELECONSULTATIONS“. Computer Science 18, Nr. 1 (2017): 95. http://dx.doi.org/10.7494/csci.2017.18.1.95.
Der volle Inhalt der QuelleYi, Fan, und Peihua Qiu. „Edge-Preserving Denoising of Image Sequences“. Entropy 23, Nr. 10 (12.10.2021): 1332. http://dx.doi.org/10.3390/e23101332.
Der volle Inhalt der QuelleAn, Dezhi, Jun Lu, Shengcai Zhang, Yan Li und António M. Lopes. „A Novel Selective Encryption Method Based on Skin Lesion Detection“. Mathematical Problems in Engineering 2020 (28.09.2020): 1–13. http://dx.doi.org/10.1155/2020/7982192.
Der volle Inhalt der QuelleMa, Bin, Bing Li, Xiao-Yu Wang, Chun-Peng Wang, Jian Li und Yun-Qing Shi. „Code Division Multiplexing and Machine Learning Based Reversible Data Hiding Scheme for Medical Image“. Security and Communication Networks 2019 (17.01.2019): 1–9. http://dx.doi.org/10.1155/2019/4732632.
Der volle Inhalt der QuelleWang, Yi Gang, Gang Yi Jiang und Mei Yu. „Study on Medical Micro-Image Mosaic with SIFT Features“. Advanced Materials Research 121-122 (Juni 2010): 476–81. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.476.
Der volle Inhalt der QuelleJiang, Huiyan, Hanqing Tan und 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.
Der volle Inhalt der QuelleHuang, Tongyuan, Jia Xu, Yuling Yang und Baoru Han. „Robust Zero-Watermarking Algorithm for Medical Images Using Double-Tree Complex Wavelet Transform and Hessenberg Decomposition“. Mathematics 10, Nr. 7 (02.04.2022): 1154. http://dx.doi.org/10.3390/math10071154.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleForsberg, 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.
Der volle Inhalt der QuelleThis 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.
Der volle Inhalt der QuelleZhang, 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.
Der volle Inhalt der QuelleSjölund, 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.
Der volle Inhalt der QuelleMhedhbi, 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.
Der volle Inhalt der QuelleHospitals 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.
Der volle Inhalt der QuelleAquesta 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.
Der volle Inhalt der QuelleThe 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.
Der volle Inhalt der QuelleGérard, 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.
Der volle Inhalt der QuelleBücher zum Thema "Medical image sequences"
L, Kamberova Gerda, und Shah Shishir Kirit 1971-, Hrsg. DNA array image analysis: Nuts & bolts. Skippack, PA: DNA Press, 2002.
Den vollen Inhalt der Quelle findenIbrahim, El-Sayed H. Heart Mechanics: Magnetic Resonance Imaging--Mathematical Modeling, Pulse Sequences, and Image Analysis. Taylor & Francis Group, 2017.
Den vollen Inhalt der Quelle findenIbrahim, El-Sayed H. Heart Mechanics: Magnetic Resonance Imaging--Mathematical Modeling, Pulse Sequences, and Image Analysis. Taylor & Francis Group, 2017.
Den vollen Inhalt der Quelle findenIbrahim, El-Sayed H. Heart Mechanics: Magnetic Resonance Imaging--Mathematical Modeling, Pulse Sequences, and Image Analysis. Taylor & Francis Group, 2017.
Den vollen Inhalt der Quelle findenIbrahim, El-Sayed H. Heart Mechanics: Magnetic Resonance Imaging--Mathematical Modeling, Pulse Sequences, and Image Analysis. Taylor & Francis Group, 2017.
Den vollen Inhalt der Quelle findenIbrahim, El-Sayed H. Heart Mechanics: Magnetic Resonance Imaging - Mathematical Modeling, Pulse Sequences and Image Analysis. Taylor & Francis Group, 2017.
Den vollen Inhalt der Quelle findenWebb, Heather. Dante, Artist of Gesture. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192866998.001.0001.
Der volle Inhalt der QuelleKamberova, Gerda, und Shishir Shah. DNA Array Image Analysis: Nuts & Bolts (Nuts & Bolts series). DNA Press, 2002.
Den vollen Inhalt der Quelle findenMitchell, Scott A. Buddhism in America. Bloomsbury Publishing Plc, 2016. http://dx.doi.org/10.5040/9781474204064.
Der volle Inhalt der QuelleBuchteile zum Thema "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.
Der volle Inhalt der QuelleRoos, Paul, und 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.
Der volle Inhalt der QuelleVicar, Tomas, Roman Jakubicek, Jiri Chmelik und 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.
Der volle Inhalt der QuelleKim, Wonjin, Wonkyeong Lee, Sun-Young Jeon, Nayeon Kang, Geonhui Jo und 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.
Der volle Inhalt der QuelleAroukatos, Nikolaos G., Kostas Manes und 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.
Der volle Inhalt der QuelleAzzabou, Noura, und 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.
Der volle Inhalt der QuelleLi, Wanyue, Yi He, Wen Kong, Jing Wang, Guohua Deng, Yiwei Chen und 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.
Der volle Inhalt der QuelleAlberti, Marina, Simone Balocco, Xavier Carrillo, Josepa Mauri und 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.
Der volle Inhalt der QuelleSundar, Hari, Ali Khamene, Liron Yatziv und 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.
Der volle Inhalt der QuelleSong, Xubo B., Andriy Myronenko, Stephen R. Plank und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Medical image sequences"
Richens, Dominic, Morris Goldberg und Brian Morton. „Multimodality workstation for cardiac image sequences“. In Medical Imaging 1993, herausgegeben von Yongmin Kim. SPIE, 1993. http://dx.doi.org/10.1117/12.146993.
Der volle Inhalt der QuelleSkrinjar, Oskar, Yi-Yu Chou und Hemant Tagare. „Transitive nonrigid image registration: application to cardiac MR image sequences“. In Medical Imaging 2004, herausgegeben von J. Michael Fitzpatrick und Milan Sonka. SPIE, 2004. http://dx.doi.org/10.1117/12.536230.
Der volle Inhalt der QuelleDanudibroto, Adriyana, Jørn Bersvendsen, Oana Mirea, Olivier Gerard, Jan D'hooge und Eigil Samset. „Image-based temporal alignment of echocardiographic sequences“. In SPIE Medical Imaging, herausgegeben von Neb Duric und Brecht Heyde. SPIE, 2016. http://dx.doi.org/10.1117/12.2216192.
Der volle Inhalt der QuelleHartmann, M., M. A. Simons, B. Yih und R. A. Kruger. „Blood Flow Determination From Fluoroscopic Image Sequences“. In Medical Imaging II, herausgegeben von Roger H. Schneider und Samuel J. Dwyer III. SPIE, 1988. http://dx.doi.org/10.1117/12.968658.
Der volle Inhalt der QuelleFarison, James B., Yong-gab Park, Qun Yu und Hong Lu. „KL transformation of spatially invariant image sequences“. In Medical Imaging 1997, herausgegeben von Kenneth M. Hanson. SPIE, 1997. http://dx.doi.org/10.1117/12.274108.
Der volle Inhalt der QuelleHiggins, William E., Andrien J. Wang und Joseph M. Reinhardt. „Semiautomatic 4D analysis of cardiac image sequences“. In Medical Imaging 1996, herausgegeben von Eric A. Hoffman. SPIE, 1996. http://dx.doi.org/10.1117/12.237878.
Der volle Inhalt der QuelleEhrhardt, Jan, Dennis Säring und Heinz Handels. „Optical flow based interpolation of temporal image sequences“. In Medical Imaging, herausgegeben von Joseph M. Reinhardt und Josien P. W. Pluim. SPIE, 2006. http://dx.doi.org/10.1117/12.652559.
Der volle Inhalt der QuelleKennedy, Jonathon M., Michael Simms, Emma Kearney, Anita Dowling, Andrew Fagan und Neil J. O'Hare. „High-speed lossless compression for angiography image sequences“. In Medical Imaging 2001, herausgegeben von Seong K. Mun. SPIE, 2001. http://dx.doi.org/10.1117/12.428103.
Der volle Inhalt der QuelleClose, Robert A., James S. Whiting, Xiaolin Da und Neal L. Eigler. „Stabilized display of coronary x-ray image sequences“. In Medical Imaging 2004, herausgegeben von Robert L. Galloway, Jr. SPIE, 2004. http://dx.doi.org/10.1117/12.535854.
Der volle Inhalt der QuelleRoos, Paul, und Max A. Viergever. „Registration And Reversible Compression Of Angiographic Image Sequences“. In 1989 Medical Imaging, herausgegeben von Samuel J. Dwyer III, R. Gilbert Jost und Roger H. Schneider. SPIE, 1989. http://dx.doi.org/10.1117/12.953279.
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