Thematische Bibliographien / Medical Images Processing

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte

Auswahl der wissenschaftlichen Literatur zum Thema „Medical Images Processing“

Autor: Grafiati
Veröffentlicht am 28. Juni 2021
Zuletzt aktualisiert am 4. Februar 2022

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Zeitschriftenartikel zum Thema "Medical Images Processing"

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Bento, Tiago, Duarte Val´erio, Pedro Teodoro und Jorge Martins. „Fractional Order Image Processing of Medical Images“. Journal of Applied Nonlinear Dynamics 6, Nr. 2 (Juni 2017): 181–91. http://dx.doi.org/10.5890/jand.2017.06.005.

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Basha, S. Saheb, und K. Satya Prasad. „Segmentation of Medical Images Using Morphological Image Processing“. i-manager's Journal on Future Engineering and Technology 4, Nr. 3 (15.04.2009): 37–45. http://dx.doi.org/10.26634/jfet.4.3.278.

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Jeong, Eun Kee. „Simple post-processing of medical images“. Yonsei Medical Journal 36, Nr. 1 (1995): 77. http://dx.doi.org/10.3349/ymj.1995.36.1.77.

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Depeursinge, A., und H. Müller. „Sensors, Medical Images and Signal Processing:“. Yearbook of Medical Informatics 18, Nr. 01 (August 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 original development in this field. Imaging and particularly multi-dimensional imaging has had in 2008 by far the largest number of publications compared to signals and sensors.
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Depeursinge, A., und H. Müller. „Sensors, Medical Images and Signal Processing:“. Yearbook of Medical Informatics 19, Nr. 01 (August 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 original development in this field. Research published in 2009 was characterized by the emergence of mature computerized diagnosis aid frameworks with assessment of input and output quality. The purpose of these systems is clearly to bring new image and signal interpretation tools to clinicians.
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Depeursinge, A., und H. Müller. „Sensors, Medical Images and Signal Processing:“. Yearbook of Medical Informatics 20, Nr. 01 (August 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 participation of the clinicians in the study design, implementation and validation of computerized diagnosis aid tools.The best paper selection of articles on sensors, signal, and imaging informatics shows examples of excellent research on methods concerning theoretically sound original development in this field.
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Rao, K. Prahlad. „Calvarial Bone Segmentation from Medical Images by Image Processing Technique“. IJARCCE 4, Nr. 12 (30.12.2015): 491–94. http://dx.doi.org/10.17148/ijarcce.2015.412115.

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Prabu Shankar, K. C., und S. Prayla Shyry. „A Survey of image pre-processing techniques for medical images“. Journal of Physics: Conference Series 1911, Nr. 1 (01.05.2021): 012003. http://dx.doi.org/10.1088/1742-6596/1911/1/012003.

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Shivajirao Shinde, Bhausaheb. „The Origins of Digital Image Processing & Application areas in Digital Image Processing Medical Images“. IOSR Journal of Engineering 1, Nr. 1 (November 2011): 66–71. http://dx.doi.org/10.9790/3021-0116671.

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Higgins, William E., und Roderick D. Swift. „Distributed system for processing 3D medical images“. Computers in Biology and Medicine 27, Nr. 2 (März 1997): 97–115. http://dx.doi.org/10.1016/s0010-4825(96)00042-x.

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Dissertationen zum Thema "Medical Images Processing"

1

Tummala, Sai Virali, und 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 an exam, speed and simplicity were emphasized in algorithm development. The results obtained correlate well with clinical data and show promise for aiding the diagnosis of spinal nerve root irritation.
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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 les médecins à décider comment traiter un polype. La solution proposée utilise une ontologie de polype et se base sur une adaptation des raisonnements standard des logiques de description pour permettre une construction semi-automatique de requêtes et d’annotation d’images. Une deuxième contribution de ce travail consiste dans la proposition d’une nouvelle approche pour le calcul de réponses relaxées des requêtes ontologiques basée sur une notion de distance entre un individu donné et une requête donnée. Cette distance est calculée en comptant le nombre d’opérations élémentaires à appliquer à une ABox afin de rendre un individu donné x, une réponse correcte à une requête. Ces opérations élémentaires sont l’ajout à ou la suppression d’une ABox, d’assertions sur des concepts atomiques (ou leur négation) et/ou des rôles atomiques. La thèse propose plusieurs sémantiques formelles pour la relaxation de requêtes et étudie les problèmes de décision et d’optimisation sous-jacents
Querying efficiently images using an image retrieval system is a long standing and challenging research problem.In the medical domain, images are increasingly produced in large quantities due their increasing interests for many medical practices such as diagnosis, report writing and teaching. This thesis proposes a semantic-based gastroenterological images annotation and retrieval system based on a new polyp ontology that can be used to support physicians to decide how to deal with a polyp. The proposed solution uses a polyp ontology and rests on an adaptation of standard reasonings in description logic to enable semi automatic construction of queries and image annotation.A second contribution of this work lies in the proposition of a new approach for computing relaxed answers of ontological queries based on a notion of an edit distance of a given individual w.r.t. a given query. Such a distance is computed by counting the number of elementary operations needed to be applied to an ABox in order to make a given individual a correct answer to a given query. The considered elementary operations are adding to or removing from an ABox, assertions on atomic concept, a negation of an atomic concept or an atomic role. The thesis proposes several formal semantics for such query approximation and investigates the underlying decision and optimisation problems
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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, und 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 CT-CT case, perfectly aligned image pairs were the starting point of our model, and random deformations were generated to create a ground truth deformation field. For the multi-modal case, synthetic CT images were generated from T2-weighted MR using a CycleGAN model, plus synthetic deformations were applied to the MR images to generate ground truth deformation fields. The synthetic deformations were created by combining a coarse and fine deformation grid, obtaining a field with deformations of different scales. Several models were trained on images of different resolutions. Their performance was benchmarked with an analytic algorithm used in an actual registration workflow. The CT-CT models were tested using image pairs created by applying synthetic deformation fields. The MR-CT models were tested using two types of test images. The first one contained synthetic CT images and MR ones deformed by synthetically generated deformation fields. The second test set contained real MR-CT image pairs. The test performance was measured using the Dice coefficient. The CT-CT models obtained Dice scores higherthan 0.82 even for the models trained on lower resolution images. Despite the fact that all MR-CT models experienced a drop in their performance, the biggest decrease came from the analytic method used as a reference, both for synthetic and real test data. This means that the deep learning models outperformed the state-of-the-art analytic benchmark method. Even though the obtained Dice scores would need further improvement to be used in a clinical setting, the results show great potential for using deep learning-based methods for multi- and mono-modal deformable image registration.
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Madaris, Aaron T. „Characterization of Peripheral Lung Lesions by Statistical Image Processing of Endobronchial Ultrasound Images“. Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1485517151147533.

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Bücher zum Thema "Medical Images Processing"

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

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Chen, Zhong, Jianguo Liu und Mingyue Ding. MIPPR 2011: Parallel processing of images and optimization and medical imaging processing. Herausgegeben von 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) und International Symposium on Multispectral Image Processing and Pattern Recognition (7th : 2011 : Guilin, China). Bellingham, Wash: SPIE, 2011.

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

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Ogiela, Marek R. Modern computational intelligence methods for the interpretation of medical images. Berlin: 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. Herausgegeben von Society of Photo-optical Instrumentation Engineers und American Association of Physicists in Medicine. Bellingham, Wash: SPIE, 2008.

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

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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. Herausgegeben von Liu Jianguo 1973-, Hua zhong gong xue yuan, National Laboratory for Multi-spectral Information Processing Technologies und SPIE (Society). Bellingham, Wash: 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. Bellingham, Wash: SPIE, 2007.

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Dougherty, Geoff, Hrsg. Medical Image Processing. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9779-1.

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Buchteile zum Thema "Medical Images Processing"

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Cree, Michael J., und Herbert F. Jelinek. „Image Analysis of Retinal Images“. In Medical Image Processing, 249–68. New York, NY: 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, 17–25. Cham: 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, 11–16. Cham: 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, 39–82. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50490-2_4.

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Costello, David P., und Patrick A. Kenny. „Fat Segmentation in Magnetic Resonance Images“. In Medical Image Processing, 89–113. New York, NY: 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, 21–38. Cham: 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, 1–7. Cham: 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, 9–10. Cham: 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, 27–81. Cham: 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, 83–119. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61340-6_6.

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Konferenzberichte zum Thema "Medical Images Processing"

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

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Sugimoto, Naozo, Hiroshi Imamura, Hiroyuki Sekiguchi und 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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Higgins, William E., und Roderick D. Swift. „Distributed system for processing multidimensional radiological images“. In Medical Imaging 1996, herausgegeben von Murray H. Loew und Kenneth M. Hanson. SPIE, 1996. http://dx.doi.org/10.1117/12.237994.

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

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

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Wilson, David L., Lawrence R. Tarbox, David B. Cist und David D. Faul. „Image Processing Of Images From Peripheral-Artery Digital Subtraction Angiography (DSA) Studies“. In Medical Imaging II, herausgegeben von Roger H. Schneider und Samuel J. Dwyer III. SPIE, 1988. http://dx.doi.org/10.1117/12.968711.

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Nur, Ruqia, und Monique Frize. „Image processing of infrared thermal images for the detection of necrotizing enterocolitis“. In SPIE Medical Imaging, herausgegeben von Sebastien Ourselin und David R. Haynor. SPIE, 2013. http://dx.doi.org/10.1117/12.2008235.

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Mitra, Sanjit K., und Tian-Hu Yu. „Enhancement of dental x-ray images by two-channel image processing“. In Medical Imaging V: Image Processing, herausgegeben von Murray H. Loew. SPIE, 1991. http://dx.doi.org/10.1117/12.45213.

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Romdhane, Feriel, Faouzi Benzarti und Hamid Amiri. „3D medical images denoising“. In 2014 First International Image Processing, Applications and Systems Conference (IPAS). IEEE, 2014. http://dx.doi.org/10.1109/ipas.2014.7043298.

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Obuhova, Natalia A., und Alexandr A. Motyko. „Methods for medical images analyzing and processing“. In 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE, 2017. http://dx.doi.org/10.1109/eiconrus.2017.7910655.

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