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Relevant bibliographies by topics / Medical Images Processing

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Academic literature on the topic 'Medical Images Processing'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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Journal articles on the topic "Medical Images Processing"

1

MOSTOVYI, V., and S. HORIASHCHENKO. "SEGMENTATION OF MEDICAL IMAGES." Herald of Khmelnytskyi National University. Technical sciences 289, no. 5 (2020): 51–56. https://doi.org/10.31891/2307-5732-2020-289-5-51-56.

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Segmentation is an integral part of the digital image processing process. It is the division or division of the image into some parts that meet the specified characteristics and characterize these areas and the image as a whole. At the segmentation stage, issues are solved that complement the standard tasks of image processing, namely coding, restoration, quality improvement. The segmentation process is considered an integral part of the tasks of image recognition, classification and identification. That is why segmentation has found its wide application in such areas as microbiology, medicine

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Jeong, Eun Kee. "Simple post-processing of medical images." Yonsei Medical Journal 36, no. 1 (1995): 77. http://dx.doi.org/10.3349/ymj.1995.36.1.77.

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Depeursinge, A., and H. Müller. "Sensors, Medical Images and Signal Processing:." Yearbook of Medical Informatics 18, no. 01 (2009): 81–83. http://dx.doi.org/10.1055/s-0038-1638643.

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Summary Objectives To summarize current excellent research in the field of medical sensor, signal and imaging informatics. Method Synopsis of the articles selected for the IMIA (International Medical Informatics Association) Yearbook 2009. Results Current research in the field of sensors, signal, and imaging informatics is characterized by theoretically sound techniques and evaluations with focus in imaging informatics. Conclusions The best paper selection of articles on sensors, signal, and imaging informatics shows examples of excellent research on methods concerning theoretically sound orig

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Depeursinge, A., and H. Müller. "Sensors, Medical Images and Signal Processing:." Yearbook of Medical Informatics 19, no. 01 (2010): 43–46. http://dx.doi.org/10.1055/s-0038-1638687.

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Summary Objectives To summarize current excellent research in the field of medical sensor, signal and imaging informatics. Method: Synopsis of the articles selected for the IMIA (International Medical Informatics Association) Yearbook 2010. Results: Current research in the field of sensor, signal, and imaging informatics is characterized by theoretically sound techniques and evaluations with focus in imaging informatics. Conclusions: The best paper selection of articles on sensors, signal, and imaging informatics shows examples of excellent research on methods concerning theoretically sound or

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Depeursinge, A., and H. Müller. "Sensors, Medical Images and Signal Processing:." Yearbook of Medical Informatics 20, no. 01 (2011): 92–95. http://dx.doi.org/10.1055/s-0038-1638744.

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SummaryTo summarize excellent research in the field of medical sensor, signal and imaging informatics published in the year 2010.Synopsis of the articles selected for the IMIA (International Medical Informatics Association) Yearbook 2011.Current research in the field of sensors, signal, and imaging informatics is characterized by theoretically sound techniques and evaluations with focus in imaging informatics. When compared to research on sensors and signals, imaging research represent the majority of published papers in 2010. Research published in 2010 was characterized by an increased partic

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Bento, Tiago, Duarte Val´erio, Pedro Teodoro, and Jorge Martins. "Fractional Order Image Processing of Medical Images." Journal of Applied Nonlinear Dynamics 6, no. 2 (2017): 181–91. http://dx.doi.org/10.5890/jand.2017.06.005.

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Higgins, William E., and Roderick D. Swift. "Distributed system for processing 3D medical images." Computers in Biology and Medicine 27, no. 2 (1997): 97–115. http://dx.doi.org/10.1016/s0010-4825(96)00042-x.

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Panchangam, Appaji, K. V. L. N. Sastry, D. V. G. L. N. Rao, B. S. DeCristofano, B. R. Kimball, and M. Nakashima. "Processing of medical images using real-time optical Fourier processing." Medical Physics 28, no. 1 (2001): 22–27. http://dx.doi.org/10.1118/1.1328079.

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Abboud, Ali J. "Shape Adaptable Medical Multimedia Processing." Open Electrical & Electronic Engineering Journal 13, no. 1 (2019): 1–18. http://dx.doi.org/10.2174/1874129001913010001.

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Introduction:Electronic medical healthcare systems are becoming the backbone of health organizations over the world. The huge amounts of medical multimedia produced by these systems especially images and videos are transmitted by the computer networks that connect these systems. The variability in the shape and texture of transferred medical multimedia data needs adaptable procedures to process these data efficiently. In other words, these procedures must adjust automatically based on the shape of region of interests in the medical multimedia images to cope with fast changes in the healthcare

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Viswanathan, Tamilarasu. "Medical Images Processing using Effectiveness of Walsh Function." Bioscience Biotechnology Research Communications 13, no. 11 (2020): 70–72. http://dx.doi.org/10.21786/bbrc/13.11/16.

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Dissertations / Theses on the topic "Medical Images Processing"

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Tummala, Sai Virali, and Veerendra Marni. "Comparison of Image Compression and Enhancement Techniques for Image Quality in Medical Images." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15360.

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Matalas, Ioannis. "Segmentation techniques suitable for medical images." Thesis, Imperial College London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339149.

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Ford, Ralph M. (Ralph Michael) 1965. "Computer-aided analysis of medical infrared images." Thesis, The University of Arizona, 1989. http://hdl.handle.net/10150/276986.

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Thermography is a useful tool for analyzing spinal nerve root irritation, but interpretation of digital infrared images is often qualitative and subjective. A new quantitative, computer-aided method for analyzing thermograms, utilizing the human dermatome map, is presented. Image processing and pattern recognition principles needed to accomplish this goal are discussed. Algorithms for segmentation, boundary detection and interpretation of thermograms are presented. An interactive, user-friendly program to perform this analysis has been developed. Due to the relatively large number of images in

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Young, N. G. "The digital processing of astronomical and medical coded aperture images." Thesis, University of Southampton, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.482729.

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Chabane, Yahia. "Semantic and flexible query processing of medical images using ontologies." Thesis, Clermont-Ferrand 2, 2016. http://www.theses.fr/2016CLF22784/document.

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L’interrogation efficace d’images en utilisant un système de recherche d’image est un problème qui a attiré l’attention de la communauté de recherche depuis une longue période. Dans le domaine médical, les images sont de plus en plus produites en grandes quantités en raison de leur intérêt croissant pour de nombreuses pratiques médicales comme le diagnostic, la rédaction de rapports et l’enseignement. Cette thèse propose un système d’annotation et recherche sémantique d’images gastroentérologiques basé sur une nouvelle ontologie des polypes qui peut être utilisée pour aider l

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O, Dobrina. "Segmentation as a part of the intelligent medical image processing." Thesis, Київ, Національний авіаційний університет, 2012. http://er.nau.edu.ua/handle/NAU/18854.

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Segmentation is one of the key tools in medical image analysis that allows an accurate recognizing and delineating individual objects (e.g. organs) on the whole image quickly and effectively. In general, segmentation technics can be divided into two main groups: methods of explicitly specifying the desired feature and algorithms where the specification is implicit. Automated segmentation of medical images is a difficult task, because the images are often noisy and contain more than a single anatomical structure with narrow distance between organ boundaries.

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Agrafiotis, Dimitris. "Three dimensional coding and visualisation of volumetric medical images." Thesis, University of Bristol, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271864.

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Zhao, Guang, and 趙光. "Automatic boundary extraction in medical images based on constrained edge merging." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31223904.

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Morton, A. S. "A knowledge-based approach to the interpretation of medical ultrasound images." Thesis, University of Brighton, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.254407.

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Cabrera, Gil Blanca. "Deep Learning Based Deformable Image Registration of Pelvic Images." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279155.

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Deformable image registration is usually performed manually by clinicians,which is time-consuming and costly, or using optimization-based algorithms, which are not always optimal for registering images of different modalities. In this work, a deep learning-based method for MR-CT deformable image registration is presented. In the first place, a neural network is optimized to register CT pelvic image pairs. Later, the model is trained on MR-CT image pairs to register CT images to match its MR counterpart. To solve the unavailability of ground truth data problem, two approaches were used. For the

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Books on the topic "Medical Images Processing"

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Koprowski, Robert. Processing Medical Thermal Images. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-61340-6.

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Koprowski, Robert. Processing of Hyperspectral Medical Images. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50490-2.

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Chen, Zhong, Jianguo Liu, and Mingyue Ding. MIPPR 2011: Parallel processing of images and optimization and medical imaging processing. Edited by Hua zhong gong xue yuan, National Key Laboratory of Science and Technology on Multi-spectral Information Processing, Guilin dian zi ke ji da xue, SPIE (Society), and International Symposium on Multispectral Image Processing and Pattern Recognition (7th : 2011 : Guilin, China). SPIE, 2011.

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Andrew, Todd-Pokropek, Viergever M. A, North Atlantic Treaty Organization. Scientific Affairs Division., and NATO Advanced Study Institute on the Formation, Handling, and Evaluation of Medical Images (1988 : Povoa de Varzim, Portugal), eds. Medical images: Formation, handling, and evaluation. Springer-Verlag, 1992.

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Ogiela, Marek R. Modern computational intelligence methods for the interpretation of medical images. Springer, 2008.

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Ogiela, Marek R. Modern computational intelligence methods for the interpretation of medical images. Springer, 2008.

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Clough, Anne V. Medical imaging 2008: Physiology, function, and structure from medical images : 17-19 February 2008, San Diego, California, USA. Edited by Society of Photo-optical Instrumentation Engineers and American Association of Physicists in Medicine. SPIE, 2008.

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International Symposium on Multispectral Image Processing and Pattern Recognition (6th 2009 Yichang Shi, China). MIPPR 2009: Medical imaging, parallel processing of images, and optimization techniques : 30 October-1 November 2009, Yichang, China. Edited by Liu Jianguo 1973-, Hua zhong gong xue yuan, National Laboratory for Multi-spectral Information Processing Technologies, and SPIE (Society). SPIE, 2009.

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International, Symposium on Multispectral Image Processing and Pattern Recognition (5th 2007 Wuhan China). MIPPR 2007: Medical imaging, parallel processing of images, and optimization techniques : 15-17 November 2007, Wuhan, China. SPIE, 2007.

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1973-, Liu Jianguo, Hua zhong gong xue yuan, National Laboratory for Multi-spectral Information Processing Technologies, and SPIE (Society), eds. MIPPR 2009: Medical imaging, parallel processing of images, and optimization techniques : 30 October-1 November 2009, Yichang, China. SPIE, 2009.

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Book chapters on the topic "Medical Images Processing"

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Cree, Michael J., and Herbert F. Jelinek. "Image Analysis of Retinal Images." In Medical Image Processing. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9779-1_11.

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Koprowski, Robert. "Image Processing." In Processing Medical Thermal Images. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61340-6_4.

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Koprowski, Robert. "Image Pre-processing." In Processing Medical Thermal Images. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61340-6_3.

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Koprowski, Robert. "Image Processing." In Processing of Hyperspectral Medical Images. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50490-2_4.

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Costello, David P., and Patrick A. Kenny. "Fat Segmentation in Magnetic Resonance Images." In Medical Image Processing. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9779-1_5.

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Koprowski, Robert. "Image Pre-Processing." In Processing of Hyperspectral Medical Images. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50490-2_3.

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Koprowski, Robert. "Introduction." In Processing Medical Thermal Images. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61340-6_1.

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Koprowski, Robert. "Image Acquisition." In Processing Medical Thermal Images. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61340-6_2.

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Koprowski, Robert. "Examples of Tailoring the Algorithm." In Processing Medical Thermal Images. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61340-6_5.

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Koprowski, Robert. "Analysis of Image Sequences." In Processing Medical Thermal Images. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61340-6_6.

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Conference papers on the topic "Medical Images Processing"

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Seitaj, Oltiana. "Processing and Classification Algorithms of Medical Images." In 2025 IEEE 6th International Conference on Image Processing, Applications and Systems (IPAS). IEEE, 2025. https://doi.org/10.1109/ipas63548.2025.10924478.

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Bai, Kaiyuan, Yaxin Li, Haotian Liu, Siyuan Zhai, Peng Tian, and Lixiao Yang. "Composite filter-based medical image processing and its application to CT images." In 2024 Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024), edited by Pier Paolo Piccaluga, Ahmed El-Hashash, and Xiangqian Guo. SPIE, 2024. http://dx.doi.org/10.1117/12.3044181.

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Malafaia, Mafalda, Thalea Schlender, Peter A. N. Bosman, and Tanja Alderliesten. "Learning multimodal explainable AI models from medical images and tabular data: proof of concept." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3040402.

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Qian, Linchen, Jiasong Chen, Linhai Ma, Timur Urakov, Weiyong Gu, and Liang Liang. "A novel attention-based network for geometry reconstruction with error estimation from medical images." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3038529.

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Toro Betancur, V. "Processing of medical images using Maple." In SPIE Defense, Security, and Sensing, edited by Šárka O. Southern. SPIE, 2013. http://dx.doi.org/10.1117/12.2014332.

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Chen, Chin-Tu, Xiaolong Ouyang, Wing H. Wong, and Xiaoping Hu. "Improvement of medical images using Bayesian processing." In Medical Imaging VI, edited by Murray H. Loew. SPIE, 1992. http://dx.doi.org/10.1117/12.59458.

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Higgins, William E., and Roderick D. Swift. "Distributed system for processing multidimensional radiological images." In Medical Imaging 1996, edited by Murray H. Loew and Kenneth M. Hanson. SPIE, 1996. http://dx.doi.org/10.1117/12.237994.

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Sugimoto, Naozo, Hiroshi Imamura, Hiroyuki Sekiguchi, and Shigeru Eiho. "Medical Image Processing in Collaboration with Medical Researchers--Imaging and Image Processing of Cardiovascular Disease Dynamic Images." In Second International Conference on Informatics Research for Development of Knowledge Society Infrastructure (ICKS'07). IEEE, 2007. http://dx.doi.org/10.1109/icks.2007.17.

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Silva, Vítor Gabriel Sgobbi Martins, Elcio H. Shiguemori, and Wellington Cardia. "IMAGE PROCESSING FOR MEDICAL EVALUATION ON NBI ENDOSCOPY IMAGES." In 10th World Congress on Computational Mechanics. Editora Edgard Blücher, 2014. http://dx.doi.org/10.5151/meceng-wccm2012-18398.

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Nur, Ruqia, and Monique Frize. "Image processing of infrared thermal images for the detection of necrotizing enterocolitis." In SPIE Medical Imaging, edited by Sebastien Ourselin and David R. Haynor. SPIE, 2013. http://dx.doi.org/10.1117/12.2008235.

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Reports on the topic "Medical Images Processing"

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Harrison, Ian J., Ned Horning, K. Koy, P. McPhearson, and Osman Wallace. An Introduction to Remote Sensing. American Museum of Natural History, 2008. http://dx.doi.org/10.5531/cbc.ncep.0174.

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Remote sensing is a technology for sampling reflected and emitted electromagnetic radiation of features on the Earth's land surface, oceans, and atmosphere. In this module, we exclude such techniques as sonar, geomagnetic and seismic sounding, as well as medical imaging, but include a wide set of techniques often known by the alternative name of Earth Observation (EO). The main objective of this module is to introduce the basic concepts of remote sensing science, focusing on the practical aspects of accessing, visualizing, and processing remotely-sensed data. Information here is targeted towar

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