Academic literature on the topic 'Biomedical images'
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Journal articles on the topic "Biomedical images"
Thanh, D. N. H., and S. D. Dvoenko. "A DENOISING OF BIOMEDICAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5/W6 (May 18, 2015): 73–78. http://dx.doi.org/10.5194/isprsarchives-xl-5-w6-73-2015.
Full textSantosh, K. C., Naved Alam, Partha Pratim Roy, Laurent Wendling, Sameer Antani, and GeorgeR Thoma. "Arrowhead detection in biomedical images." Electronic Imaging 2016, no. 17 (February 17, 2016): 1–7. http://dx.doi.org/10.2352/issn.2470-1173.2016.17.drr-054.
Full textKundu, Amlan. "Local segmentation of biomedical images." Computerized Medical Imaging and Graphics 14, no. 3 (May 1990): 173–83. http://dx.doi.org/10.1016/0895-6111(90)90057-i.
Full textZhang, Yinghui, Fengyuan Zhang, Yantong Cui, and Ruoci Ning. "CLASSIFICATION OF BIOMEDICAL IMAGES USING CONTENT BASED IMAGE RETRIEVAL SYSTEMS." International Journal of Engineering Technologies and Management Research 5, no. 2 (February 8, 2020): 181–89. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.161.
Full textBadaoui, S., V. Chameroy, and F. Aubry. "A database manager of biomedical images." Medical Informatics 18, no. 1 (January 1993): 23–33. http://dx.doi.org/10.3109/14639239309034465.
Full textGil, Debora, Aura Hernàndez-Sabaté, Mireia Brunat, Steven Jansen, and Jordi Martínez-Vilalta. "Structure-preserving smoothing of biomedical images." Pattern Recognition 44, no. 9 (September 2011): 1842–51. http://dx.doi.org/10.1016/j.patcog.2010.08.003.
Full textVitulano, S., C. Di Ruberto, and M. Nappi. "Different methods to segment biomedical images." Pattern Recognition Letters 18, no. 11-13 (November 1997): 1125–31. http://dx.doi.org/10.1016/s0167-8655(97)00097-4.
Full textTaratorin, A. M., E. E. Godik, and Yu V. Guljaev. "Functional mapping of dynamic biomedical images." Measurement 8, no. 3 (July 1990): 137–40. http://dx.doi.org/10.1016/0263-2241(90)90055-b.
Full textSakr, Majd, Mohammad Hammoud, and Manoj Dareddy Reddy. "Image processing on the Cloud: Characterizing edge detection on biomedical images." Qatar Foundation Annual Research Forum Proceedings, no. 2012 (October 2012): CSPS11. http://dx.doi.org/10.5339/qfarf.2012.csps11.
Full textKorenblum, Daniel, Daniel Rubin, Sandy Napel, Cesar Rodriguez, and Chris Beaulieu. "Managing Biomedical Image Metadata for Search and Retrieval of Similar Images." Journal of Digital Imaging 24, no. 4 (September 16, 2010): 739–48. http://dx.doi.org/10.1007/s10278-010-9328-z.
Full textDissertations / Theses on the topic "Biomedical images"
Pham, Hong Nhung. "Graph-based registration for biomedical images." Thesis, Poitiers, 2019. http://www.theses.fr/2019POIT2258/document.
Full textThe context of this thesis is the image registration for endomicroscopic images. Multiphoton microendoscope provides different scanning trajectories which are considered in this work. First we propose a nonrigid registration method whose motion estimation is cast into a feature matching problem under the Log-Demons framework using Graph Wavelets. We investigate the Spectral Graph Wavelets (SGWs) to capture the shape feature of the images. The data representation on graphs is more adapted to data with complex structures. Our experiments on endomicroscopic images show that this method outperforms the existing nonrigid image registration techniques. We then propose a novel image registration strategy for endomicroscopic images acquired on irregular grids. The Graph Wavelet transform is flexible to apply on different types of data regardless of the data point densities and how complex the data structure is. We also show how the Log-Demons framework can be adapted to the optimization of the objective function defined for images with an irregular sampling
RUNDO, LEONARDO. "Computer-Assisted Analysis of Biomedical Images." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241343.
Full textNowadays, the amount of heterogeneous biomedical data is increasing more and more thanks to novel sensing techniques and high-throughput technologies. In reference to biomedical image analysis, the advances in image acquisition modalities and high-throughput imaging experiments are creating new challenges. This huge information ensemble could overwhelm the analytic capabilities needed by physicians in their daily decision-making tasks as well as by biologists investigating complex biochemical systems. In particular, quantitative imaging methods convey scientifically and clinically relevant information in prediction, prognosis or treatment response assessment, by also considering radiomics approaches. Therefore, the computational analysis of medical and biological images plays a key role in radiology and laboratory applications. In this regard, frameworks based on advanced Machine Learning and Computational Intelligence can significantly improve traditional Image Processing and Pattern Recognition approaches. However, conventional Artificial Intelligence techniques must be tailored to address the unique challenges concerning biomedical imaging data. This thesis aims at proposing novel and advanced computer-assisted methods for biomedical image analysis, also as an instrument in the development of Clinical Decision Support Systems, by always keeping in mind the clinical feasibility of the developed solutions. The devised classical Image Processing algorithms, with particular interest to region-based and morphological approaches in biomedical image segmentation, are first described. Afterwards, Pattern Recognition techniques are introduced, applying unsupervised fuzzy clustering and graph-based models (i.e., Random Walker and Cellular Automata) to multispectral and multimodal medical imaging data processing. Taking into account Computational Intelligence, an evolutionary framework based on Genetic Algorithms for medical image enhancement and segmentation is presented. Moreover, multimodal image co-registration using Particle Swarm Optimization is discussed. Finally, Deep Neural Networks are investigated: (i) the generalization abilities of Convolutional Neural Networks in medical image segmentation for multi-institutional datasets are addressed by conceiving an architecture that integrates adaptive feature recalibration blocks, and (ii) the generation of realistic medical images based on Generative Adversarial Networks is applied to data augmentation purposes. In conclusion, the ultimate goal of these research studies is to gain clinically and biologically useful insights that can guide differential diagnosis and therapies, leading towards biomedical data integration for personalized medicine. As a matter of fact, the proposed computer-assisted bioimage analysis methods can be beneficial for the definition of imaging biomarkers, as well as for quantitative medicine and biology.
Cai, Hongmin. "Quality enhancement and segmentation for biomedical images." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B39380130.
Full textCai, Hongmin, and 蔡宏民. "Quality enhancement and segmentation for biomedical images." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39380130.
Full textLashin, Nabil Aly Mohamed Aly. "Restoration methods for biomedical images in confocal microscopy." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=975678167.
Full textAguilar, Chongtay María del Rocío. "Model based system for automated analysis of biomedical images." Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/30059.
Full textStanier, Jeffrey. "Segmentation and editing of 3-dimensional medical images." Thesis, University of Ottawa (Canada), 1994. http://hdl.handle.net/10393/10031.
Full textStinson, Eric. "Distortion correction for diffusion weighted magnetic resonance images." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32587.
Full textL'imagerie par résonance magnétique (IRM) de diffusion est utile dans l'étude du cerveau humain, tant en santé que dysfonctionnel ou atteint de maladie. Malheureusement, cette technique est susceptible à des distortions géometriques qui diminuent la précision et la valeur des données. Un algorithme de correction de ces distortions doit être utilisé pendant le traitement des données. Le but de ce mémoire est de développer, d'implementer et de tester une méthode de correction des distortions pour l'IRM de diffusion. Un algorithme de correction des distortions fut developé et implémenté, puis évalué sur des ensembles de données cérébrales humaines simulées et réelles. L'algorithme fonctionne bien pour des données simulées avec des valeurs b jusqu'à b=2000 s/(mm*mm). La cause des échecs de la correction de distortion fut également étudiée. Les échecs sont attribués à une combinaison de la réduction du rapport signal sur bruit (SNR, pour signal-to-noise ratio) et de l'augmentation des différences de contraste, dans les ensembles de données avec des valeurs-b plus élevées.
Chen, Pei. "Volumetric reconstruction and real-time deformation modeling of biomedical images." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 6.09 Mb., p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3220796.
Full textPrincipal faculty advisors: Kenneth E. Barner, Dept. of Electrical and Computer Engineering; and Karl V. Steiner, Delaware Biotechnology Institute. Includes bibliographical references.
Selagamsetty, Srinivasa Siddhartha. "Exploring a Methodology for Segmenting Biomedical Images using Deep Learning." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573812579683504.
Full textBooks on the topic "Biomedical images"
Nat-Ali, Amine, and Christine Cavaro-Mnard, eds. Compression of Biomedical Images and Signals. London, UK: ISTE, 2008. http://dx.doi.org/10.1002/9780470611159.
Full textAmine, Nait-Ali, and Cavaro-Menard Christine, eds. Compression of biomedical images and signals. London: ISTE, 2008.
Find full textTodman, Alison Grant. Low-level grouping mechanisms for contour completion in biomedical images. Birmingham: University of Birmingham, 1998.
Find full text1954-, Edwards Jeanette, Harvey Penelope 1956-, and Wade Peter 1957-, eds. Technologized images, technologized bodies. New York: Berghahn Books, 2010.
Find full textHabib, Zaidi, ed. Quantitative analysis of nuclear medicine images. New York: Springer, 2005.
Find full textManfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: Firenze University Press, 2013. http://dx.doi.org/10.36253/978-88-6655-470-7.
Full textManfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: Firenze University Press, 2009. http://dx.doi.org/10.36253/978-88-6453-096-3.
Full textManfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: Firenze University Press, 2011. http://dx.doi.org/10.36253/978-88-6655-011-2.
Full textIEEE Engineering in Medicine and Biology Society. Conference. Images of the twenty-first century: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Seattle, Washington, November 9-12, 1989. New York, NY (345 E. 47th St., New York 10017): Institute of Electrical and Electronic Engineers, 1989.
Find full textIEEE Engineering in Medicine and Biology Society. Conference. Images of the twenty-first century: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Seattle, Washington, November 9-12, 1989. New York, N.Y: IEEE, 1989.
Find full textBook chapters on the topic "Biomedical images"
Petrou, Maria. "Texture in Biomedical Images." In Biomedical Image Processing, 157–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15816-2_6.
Full textBoehler, Tobias, Kathy Schilling, Ulrich Bick, and Horst K. Hahn. "Deformable Image Registration of Follow-Up Breast Magnetic Resonance Images." In Biomedical Image Registration, 13–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14366-3_2.
Full textJames, J., and H. J. Tanke. "Reproduction of microscopic images, microphotography." In Biomedical Light Microscopy, 102–26. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3778-2_5.
Full textJames, J., and H. J. Tanke. "Quantitative analysis of microscopic images." In Biomedical Light Microscopy, 127–58. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3778-2_6.
Full textCao, Chuqing, Chao Li, and Ying Sun. "Motion Tracking in Medical Images." In Biomedical Image Understanding, 229–74. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781118715321.ch7.
Full textPitiot, Alain, Grégoire Malandain, Eric Bardinet, and Paul M. Thompson. "Piecewise Affine Registration of Biological Images." In Biomedical Image Registration, 91–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39701-4_10.
Full textWodzinski, Marek, and Henning Müller. "Learning-Based Affine Registration of Histological Images." In Biomedical Image Registration, 12–22. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50120-4_2.
Full textAvants, Brian, Elliot Greenblatt, Jacob Hesterman, and Nicholas Tustison. "Deep Volumetric Feature Encoding for Biomedical Images." In Biomedical Image Registration, 91–100. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50120-4_9.
Full textVansteenkiste, Ewout, Jef Vandemeulebroucke, and Wilfried Philips. "2D/3D Registration of Neonatal Brain Images." In Biomedical Image Registration, 272–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11784012_33.
Full textLuu, Manh Ha, Hassan Boulkhrif, Adriaan Moelker, and Theo van Walsum. "Registration Evaluation by De-enhancing CT Images." In Biomedical Image Registration, 83–93. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92258-4_8.
Full textConference papers on the topic "Biomedical images"
Sheng, Jianqiang, Songhua Xu, Weicai Deng, and Xiaonan Luo. "Novel image features for categorizing biomedical images." In 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2012. http://dx.doi.org/10.1109/bibm.2012.6392689.
Full textRanji, Mahsa, Diego Calzolari, Ramses Agustin, and Jeff H. Price. "Is image cytometry possible with deconvolved fluorescence images?" In Biomedical Optics. Washington, D.C.: OSA, 2010. http://dx.doi.org/10.1364/biomed.2010.btud84.
Full textSuresha, H. S., and N. S. Chandrashekar. "Analysis of Ultrasound Images & Biomedical Images Using Digital Image Processing." In Second International Conference on Signal Processing, Image Processing and VLSI. Singapore: Research Publishing Services, 2015. http://dx.doi.org/10.3850/978-981-09-6200-5_d-50.
Full textKulkami, Shirish S., Bhavesh B. Digey, R. N. Awale, and Abhay Wagh. "Image registration on biomedical images with composite algorithm." In 2017 International Conference on Nascent Technologies in Engineering (ICNTE). IEEE, 2017. http://dx.doi.org/10.1109/icnte.2017.7947951.
Full textSantosh, K. C., Laurent Wendling, Sameer K. Antani, and George R. Thoma. "Scalable Arrow Detection in Biomedical Images." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.561.
Full textHsiao, Han C. W., Rouh-Mei Hu, Wei-Liang Tai, Rong-Ming Chen, and Jeffrey J. P. Tsai. "Object Relational Programming of Biomedical Images." In Bioengineering (BIBE). IEEE, 2011. http://dx.doi.org/10.1109/bibe.2011.15.
Full textWang, James Z. "Region-based retrieval of biomedical images." In the eighth ACM international conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/354384.376492.
Full textAntonenko, Yevhenii A., Timofey N. Mustetsov, Rami R. Hamdi, Teresa Małecka-Massalska, Nurbek Orshubekov, Róża Dzierżak, and Svetlana Uvaysova. "Double-compression method for biomedical images." In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2017, edited by Ryszard S. Romaniuk and Maciej Linczuk. SPIE, 2017. http://dx.doi.org/10.1117/12.2280989.
Full textCoatrieux, Gouenou, Henri Maitre, and Bulent Sankur. "Strict integrity control of biomedical images." In Photonics West 2001 - Electronic Imaging, edited by Ping W. Wong and Edward J. Delp III. SPIE, 2001. http://dx.doi.org/10.1117/12.435403.
Full textGhebreab, Sennay, Carl Jaffe, and Arnold W. M. Smeulders. "Concept-based retrieval of biomedical images." In Medical Imaging 2003, edited by H. K. Huang and Osman M. Ratib. SPIE, 2003. http://dx.doi.org/10.1117/12.487796.
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