Готові списки джерел за темами / Biomedical images

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Добірка наукової літератури з теми "Biomedical images"

Автор: Grafiati
Опубліковано: 10 березня 2023

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Статті в журналах з теми "Biomedical images"

1

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.

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Анотація:
Today imaging science has an important development and has many applications in different fields of life. The researched object of imaging science is digital image that can be created by many digital devices. Biomedical image is one of types of digital images. One of the limits of using digital devices to create digital images is noise. Noise reduces the image quality. It appears in almost types of images, including biomedical images too. The type of noise in this case can be considered as combination of Gaussian and Poisson noises. In this paper we propose method to remove noise by using total variation. Our method is developed with the goal to combine two famous models: ROF for removing Gaussian noise and modified ROF for removing Poisson noise. As a result, our proposed method can be also applied to remove Gaussian or Poisson noise separately. The proposed method can be applied in two cases: with given parameters (generated noise for artificial images) or automatically evaluated parameters (unknown noise for real images).
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2

Santosh, 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.

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3

Kundu, 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.

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4

Zhang, 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.

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Анотація:
Because of the numerous application of Content-based image retrieval (CBIR) system in various areas, it has always remained a topic of keen interest by the researchers. Fetching of the most similar image from the complete repository by comparing it to the input image in the minimum span of time is the main task of the CBIR. The purpose of the CBIR can vary from different types of requirements like a diagnosis of the illness by the physician, crime investigation, product recommendation by the e-commerce companies, etc. In the present work, CBIR is used for finding the similar patients having Breast cancer. Gray-Level Co-Occurrence Matrix along with histogram and correlation coefficient is used for creating CBIR system. Comparing the images of the area of interest of a present patient with the complete series of the image of a past patient can help in early diagnosis of the disease. CBIR is so much effective that even when the symptoms are not shown by the body the disease can be diagnosed from the sample images.
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Badaoui, 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.

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Gil, 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.

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Vitulano, 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.

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Taratorin, 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.

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Sakr, 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.

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Korenblum, 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.

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Дисертації з теми "Biomedical images"

1

Pham, Hong Nhung. "Graph-based registration for biomedical images." Thesis, Poitiers, 2019. http://www.theses.fr/2019POIT2258/document.

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Анотація:
Le contexte de cette thèse est le recalage d'images endomicroscopiques. Le microendoscope multiphotonique fournit différentes trajectoires de balayage que nous considérons dans ce travail. Nous proposons d'abord une méthode de recalage non rigide dont l'estimation du mouvement est transformée en un problème d'appariement d'attributs dans le cadre des Log-Demons et d'ondelettes sur graphes. Nous étudions les ondelettes de graphe spectral (SGW) pour capturer les formes des images, en effet, la représentation des données sur les graphes est plus adaptée aux données avec des structures complexes. Nos expériences sur des images endomicroscopiques montrent que cette méthode est supérieure aux techniques de recalage d'images non rigides existantes. Nous proposons ensuite une nouvelle stratégie de recalage d'images pour les images endomicroscopiques acquises sur des grilles irrégulières. La transformée en ondelettes sur graphe est flexible et peut être appliquée à différents types de données, quelles que soient la densité de points et la complexité de la structure de données. Nous montrons également comment le cadre des Log-Demons peut être adapté à l'optimisation de la fonction objective définie pour les images acquises avec un échantillonnage irrégulier
The 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
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RUNDO, LEONARDO. "Computer-Assisted Analysis of Biomedical Images." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241343.

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Анотація:
Oggigiorno, la mole di dati biomedicali eterogenei è in continua crescita grazie alle nuove tecniche di sensing e alle tecnologie ad high-throughput. Relativamente all'analisi di immagini biomedicali, i progressi relativi alle modalità di acquisizione di immagini agli esperimenti di imaging ad high-throughput stanno creando nuove sfide. Questo ingente complesso di informazioni può spesso sopraffare le capacità analitiche sia dei medici nei loro processi decisionali sia dei biologi nell'investigazione di sistemi biochimici complessi. In particolare, i metodi di imaging quantitativo forniscono informazioni scientificamente rilevanti per la predizione, la prognosi o la valutazione della risposta al trattamento, prendendo in considerazione anche approcci di radiomica. Pertanto, l'analisi computazionale di immagini medicali e biologiche svolge un ruolo chiave in applicazioni di radiologia e di laboratorio. A tal proposito, framework basati su tecniche avanzate di Machine Learning e Computational Intelligence permettono di migliorare significativamente i tradizionali approcci tradizionali di Image Processing e Pattern Recognition. Tuttavia, le tecniche convenzionali di Intelligenza Artificiale devono essere propriamente adattate alle sfide uniche imposte dai dati di imaging biomedicale. La presente tesi mira a proporre innovativi metodi assistiti da calcolatore per l'analisi di immagini biomedicali, da utilizzare anche come strumento per lo sviluppo di Sistemi di Supporto alle Decisioni Cliniche, tenendo sempre in considerazione la fattibilità delle soluzioni sviluppate. In primo luogo, sono descritti gli algoritmi classici di Image Processing realizzati, focalizzandosi sugli approcci basati su regioni e sulla morfologia matematica. Dopodiché, si introducono le tecniche di Pattern Recognition, applicando il clustering fuzzy non supervisionato e i modelli basati su grafi (i.e., Random Walker e Automi Cellulari) per l'elaborazione di dati multispettrali e multimodali di imaging medicale. In riferimento ai metodi di Computational Intelligence, viene presentato un innovativo framework evolutivo basato sugli Algoritmi Genetici per il miglioramento e la segmentazione di immagini medicali. Inoltre, è discussa la co-registrazione di immagini multimodali utilizzando Particle Swarm Optimization. Infine, si investigano le Deep Neural Network: (i) le capacità di generalizzazione delle Convolutional Neural Network nell'ambito della segmentazione di immagini medicali provenienti da studi multi-istituzionali vengono affrontate mediante la progettazione di un'architettura che integra blocchi di ricalibrazione delle feature, e (ii) la generazione di immagini medicali realistiche basata sulle Generative Adversarial Network è applicata per scopi di data augmentation. In conclusione, il fine ultimo di tali studi è quello di ottenere conoscenza clinicamente e biologicamente utile che possa guidare le diagnosi e le terapie differenziali, conducendo verso l'integrazione di dati biomedicali per la medicina personalizzata. Difatti, i metodi assistiti da calcolatore per l'analisi delle immagini biomedicali sono vantaggiosi sia per la definizione di biomarcatori basati sull'imaging sia per la medicina e biologia quantitativa.
Nowadays, 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.
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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.

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Cai, 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.

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Lashin, 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.

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Aguilar, Chongtay María del Rocío. "Model based system for automated analysis of biomedical images." Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/30059.

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Анотація:
This thesis is concerned with developing a probabilistic formulation of model-based vision using generalised flexible template models. It includes the design and implementation of a system which extends flexible template models to include grey level information in the object representation for image interpretation. This system was designed to deal with microscope images where the different stain and illumination conditions during the image acquisition process produce a strong correlation between density profile and geometric shape. This approach is based on statistical knowledge from a training set of examples. The variability of the shape-grey level relationships is characterised by applying principal component analysis to the shape-grey level vector extracted from the training set. The main modes of variation of each object class are encoded with a generic object formulation constrained by the training set limits. This formulation adapts to the diversity and irregularities of shape and view during the object recognition process. The modes of variation are used to generate new object instances for the matching process of new image data. A genetic algorithm method is used to find the best possible explanation for a candidate of a given model, based on the probability distribution of all possible matches. This approach is demonstrated by its application to microscope images of brain cells. It provides the means to obtain information such as brain cells density and distribution. This information could be useful in the understanding of the development and properties of some Central Nervous System (CNS) related diseases, such as in studies on the effects of HIV in CNS where neuronal loss is expected. The performance of the SGmodel system was compared with manual neuron counts from domain experts. The results show no significant difference between SGmodel and manual neuron estimates. The observation of bigger differences between the counts of the domain experts underlines the automated approach importance to perform an objective analysis.
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7

Stanier, Jeffrey. "Segmentation and editing of 3-dimensional medical images." Thesis, University of Ottawa (Canada), 1994. http://hdl.handle.net/10393/10031.

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Анотація:
Neuroradiologists rely on scanned images of the human brain to diagnose many pathologies. The images, even those collected in 3-dimensions, are typically displayed as a 2-dimensional collage of slices and much of the intrinsic 3-D structure of the data is lost. Image Atlases are commonly used to delineate and label Volumes Of Interest (VOIs) in 3-dimensional, slice-type, medical data sets. They can serve many purposes: to highlight important regions, to quantify the size and shape of structures in the images, to define a surface for 3-D rendering and to help in navigation through a series of images. To perform these functions, an individual atlas is required for each data set. The purpose of this thesis is to develop a link between the volume data and the individual atlas associated with each set of images. An automatic method of building an individual atlas from the volume data is proposed. The method uses a data-driven, bottom-up segmentation to produce a primitive atlas followed by a knowledge-driven, top-down merging and labelling stage to refine the primitive atlas into an individual atlas. The system was implemented in software using an object-oriented approach which allowed for a high quality user interface and a flexible and efficient implementation of the concepts of an atlas and a VOI. Tests were performed to judge the quality of the segmentations and of the atlas labellings. The results prove that the individual atlases created using the proposed method are sufficiently accurate to aid in visualizing 3-D structures in medical data sets and to quantify the sizes of these structures.
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Stinson, 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.

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Diffusion magnetic resonance imaging (MRI) is useful for studying the diseased, dysfunctional, and healthy human brain. Unfortunately, this technique is susceptible to geometric distortions that decrease the accuracy and value of the data. A distortion correction algorithm must be used to remedy these issues during post-processing. The purpose of this thesis is to develop, implement, and test a distortion correction method for diffusion weighted MRI. A distortion correction algorithm was designed and implemented and then tested on simulated and real human brain datasets. The algorithm was found to work well for simulated datasets with b-values up to and including b=2000 s/(mm*mm). Furthermore, the cause of distortion correction failures were investigated. Failures are believed to be due to a combination of reduced signal to noise ratio (SNR) and increased contrast differences in datasets with higher b-values.
L'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.
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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.

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Анотація:
Thesis (Ph.D.)--University of Delaware, 2006.
Principal faculty advisors: Kenneth E. Barner, Dept. of Electrical and Computer Engineering; and Karl V. Steiner, Delaware Biotechnology Institute. Includes bibliographical references.
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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.

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Книги з теми "Biomedical images"

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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.

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Amine, Nait-Ali, and Cavaro-Menard Christine, eds. Compression of biomedical images and signals. London: ISTE, 2008.

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3

Todman, Alison Grant. Low-level grouping mechanisms for contour completion in biomedical images. Birmingham: University of Birmingham, 1998.

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4

1954-, Edwards Jeanette, Harvey Penelope 1956-, and Wade Peter 1957-, eds. Technologized images, technologized bodies. New York: Berghahn Books, 2010.

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5

Habib, Zaidi, ed. Quantitative analysis of nuclear medicine images. New York: Springer, 2005.

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6

Manfredi, 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.

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The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies.
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Manfredi, 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.

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Анотація:
The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies.
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Manfredi, 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.

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Анотація:
The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies.
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IEEE 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.

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10

IEEE 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.

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Частини книг з теми "Biomedical images"

1

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.

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2

Boehler, 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.

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James, 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.

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James, 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.

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Cao, 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.

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Pitiot, 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.

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7

Wodzinski, 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.

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8

Avants, 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.

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9

Vansteenkiste, 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.

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Luu, 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.

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Тези доповідей конференцій з теми "Biomedical images"

1

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.

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2

Ranji, 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.

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3

Suresha, 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.

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4

Kulkami, 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.

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5

Santosh, 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.

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6

Hsiao, 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.

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7

Wang, 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.

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8

Antonenko, 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.

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9

Coatrieux, 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.

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10

Ghebreab, 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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