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Relevant bibliographies by topics / Three-dimensional image in medicine

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Academic literature on the topic 'Three-dimensional image in medicine'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Three-dimensional image in medicine"

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Mankovich, Nicholas J., Douglas R. Robertson, and Andrew M. Cheeseman. "Three-dimensional image display in medicine." Journal of Digital Imaging 3, no. 2 (May 1990): 69–80. http://dx.doi.org/10.1007/bf03170565.

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Wang, Jiang Wei. "An Improved Three-Dimensional Medical Image Segmentation Approach." Advanced Materials Research 912-914 (April 2014): 1150–55. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1150.

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This paper presents an three-dimensional medical image segmentation approach based on Live-Wire algorithm, through the virtual slices extraction to transform the direction of the segmentation from parallel direction into meridian direction, the amount of user interaction is independent of the number of the sequence of medicine images, improves the automation level of medicine images segmentation. This paper also improved the efficiency of traditional Live-Wire algorithm by four binary heap. Experiment shows that the approach can segmented the interest objects from the sequence of medical images rapidly and accurately, with less user interaction.

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Rigaut, Jean Paul, Jany Vassy, Gustavo Linares-Cruz, and Angela M. Downs. "THREE-DIMENSIONAL IMAGE CYTOMETRY." Biology of the Cell 79, no. 3 (1993): 297. http://dx.doi.org/10.1016/0248-4900(93)90238-a.

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Boag, A. H., L. A. Kennedy, and M. J. Miller. "Three-Dimensional Microscopic Image Reconstruction of Prostatic Adenocarcinoma." Archives of Pathology & Laboratory Medicine 125, no. 4 (April 1, 2001): 562–66. http://dx.doi.org/10.5858/2001-125-0562-tdmiro.

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Abstract Context.—Routine microscopy provides only a 2-dimensional view of the complex 3-dimensional structure that makes up human tissue. Three-dimensional microscopic image reconstruction has not been described previously for prostate cancer. Objectives.—To develop a simple method of computerized 3-dimensional image reconstruction and to demonstrate its applicability to the study of prostatic adenocarcinoma. Methods.—Serial sections were cut from archival paraffin-embedded prostate specimens, immunostained using antikeratin CAM5.2, and digitally imaged. Computer image–rendering software was used to produce 3-dimensional image reconstructions of prostate cancer of varying Gleason grades, normal prostate, and prostatic intraepithelial neoplasia. Results.—The rendering system proved easy to use and provided good-quality 3-dimensional images of most specimens. Normal prostate glands formed irregular fusiform structures branching off central tubular ducts. Prostatic intraepithelial neoplasia showed external contours similar to those of normal glands, but with a markedly complex internal arrangement of branching lumens. Gleason grade 3 carcinoma was found to consist of a complex array of interconnecting tubules rather than the apparently separate glands seen in 2 dimensions on routine light microscopy. Gleason grade 4 carcinoma demonstrated a characteristic form of glandular fusion that was readily visualized by optically sectioning and rotating the reconstructed images. Conclusions.—Computerized 3-dimensional microscopic imaging holds great promise as an investigational tool. By revealing the structural relationships of the various Gleason grades of prostate cancer, this method could be used to refine diagnostic and grading criteria for this common tumor.

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Grande, A. M. "Heterotopic heart transplantation: three dimensional image." Heart 91, no. 9 (September 1, 2005): 1172. http://dx.doi.org/10.1136/hrt.2004.053322.

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Kashiwagi, Katsuya, and Katsumi Kose. "A method to extract three-dimensional objects from three-dimensional NMR image data." NMR in Biomedicine 10, no. 1 (January 1997): 13–17. http://dx.doi.org/10.1002/(sici)1099-1492(199701)10:1<13::aid-nbm443>3.0.co;2-z.

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Pan, T. "Fundamentals of Three-Dimensional Digital Image Processing." Journal of Nuclear Medicine 51, no. 6 (May 19, 2010): 995. http://dx.doi.org/10.2967/jnumed.109.074476.

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Sheppard, C. J. R., and C. J. Cogswell. "Three-dimensional image formation in confocal microscopy." Journal of Microscopy 159, no. 2 (August 1990): 179–94. http://dx.doi.org/10.1111/j.1365-2818.1990.tb04774.x.

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Nijhawan, Romi. "‘Reversed’ Illusion with Three-Dimensional Müller-Lyer Shapes." Perception 24, no. 11 (November 1995): 1281–96. http://dx.doi.org/10.1068/p241281.

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The purpose of this study was to determine whether the Müller-Lyer illusion is produced by a mechanism which uses information defined in the retinal coordinates, or by a mechanism taking into account the three-dimensional (3-D) shape of the illusion figure. The classical Müller-Lyer figure could not be used to address this question since it is two-dimensional. Three-dimensional Müller-Lyer figures were created to see if the illusion they produce is correlated with the shape of the projected retinal image, or with the shape of these figures defined in a 3-D coordinate frame. In the experiments retinal image shape was juxtaposed against the 3-D shape of the illusion displays. For some displays the direction in which the fins pointed, relative to the shafts, in the 3-D frame was the ‘opposite’ of the direction in which they pointed in the retinal images. For such displays, the illusion predicted on the basis of the 3-D structure was the opposite of that predicted on the basis of retinal image shapes. For another 3-D display the fins were oriented such that each projected a single straight line in the retinal image, thus the typical retinal image (< >, > <) was replaced by straight lines (‖, ‖). For all the displays the observed illusion was consistent with how the fins were oriented relative to the shaft in the 3-D coordinate frame, ie with the 3-D shape of the illusion displays. The retinal image shape appeared to play little, if any, role. One conclusion that emerges is that the specific retinal image shape projected by the classical line-drawn pattern is neither necessary nor sufficient for producing the illusion. The present findings are inconsistent with two well known theories of the Müller-Lyer illusion: inappropriate constancy scaling and selective filtering.

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Sun, Ke, and Cunwei Lu. "Three-Dimensional Image Measurement by Pattern Projection Using a Single Observation Image." Cybernetics and Information Technologies 15, no. 6 (December 1, 2015): 29–45. http://dx.doi.org/10.1515/cait-2015-0065.

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Abstract Since three-dimensional image measurement allows object surface shapes and dimensions to be obtained quickly and without any contact, it has recently been intensively studied in a wide range of fields, including industry, medicine and security. Three-dimensional image measurement technologies can be broadly classified into passive techniques, such as stereovision and active techniques, such as patterned light projection. Among these, the method of projecting optimum intensity modulated light patterns for three-dimensional image measurement can obtain three-dimensional information on the measured object with a single projection, so it is expected to be highly applicable in practice. Measurement can be performed using a single observation image when the object to be measured has simple colouration or surface reflectivity, but for complex objects, eliminating the influence of colour and surface reflectivity requires a reference image to correct the intensity of the observed pattern. To address this, we propose an analysis method and image correction technology, using a novel colour system for realizing three-dimensional measurements using only one observation image.

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Dissertations / Theses on the topic "Three-dimensional image in medicine"

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Simpson, David Elliott. "Maximum entropy image processing in two and three dimensional single photon nuclear medicine imaging." Thesis, University of Southampton, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259973.

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Prasai, Persis. "Multimodality image registration." Birmingham, Ala. : University of Alabama at Birmingham, 2006. http://www.mhsl.uab.edu/dt/2007m/prasai.pdf.

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Hinshaw, Kevin P. "Seeing structure : using knowledge to reconstruct and illustrate anatomy /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/6882.

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Baum, Karl G. "Multimodal breast imaging : registration, visualization, and image synthesis /." Online version of thesis, 2008. http://hdl.handle.net/1850/7063.

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Kang, Xin, and 康欣. "Feature-based 2D-3D registration and 3D reconstruction from a limited number of images via statistical inference for image-guidedinterventions." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B48079625.

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Traditional open interventions have been progressively replaced with minimally invasive techniques. Most notably, direct visual feedback is transitioned into indirect, image-based feedback, leading to the wide use of image-guided interventions (IGIs). One essential process of all IGIs is to align some 3D data with 2D images of patient through a procedure called 3D-2D registration during interventions to provide better guidance and richer information. When the 3D data is unavailable, a realistic 3D patient-speci_c model needs to be constructed from a few 2D images. The dominating methods that use only image intensity have narrow convergence range and are not robust to foreign objects presented in 2D images but not existed in 3D data. Feature-based methods partly addressed these problems, but most of them heavily rely on a set of \best" paired correspondences and requires clean image features. Moreover, the optimization procedures used in both kinds of methods are not e_cient. In this dissertation, two topics have been studied and novel algorithms proposed, namely, contour extraction from X-ray images and feature-based rigid/deformable 3D-2D registration. Inspired by biological and neuropsychological characteristics of primary visual cortex (V1), a contour detector is proposed for simultaneously extracting edges and lines in images. The synergy of V1 neurons is mimicked using phase congruency and tensor voting. Evaluations and comparisons showed that the proposed method outperformed several commonly used methods and the results are consistent with human perception. Moreover, the cumbersome \_ne-tuning" of parameter values is not always necessary in the proposed method. An extensible feature-based 3D-2D registration framework is proposed by rigorously formulating the registration as a probability density estimation problem and solving it via a generalized expectation maximization algorithm. It optimizes the transformation directly and treats correspondences as nuisance parameters. This is signi_cantly di_erent from almost all feature-based method in the literature that _rst single out a set of \best" correspondences and then estimate a transformation associated with it. This property makes the proposed algorithm not rely on paired correspondences and thus inherently robust to outliers. The framework can be adapted as a point-based method with the major advantages of 1) independency on paired correspondences, 2) accurate registration using a single image, and 3) robustness to the initialization and a large amount of outliers. Extended to a contour-based method, it di_ers from other contour-based methods mainly in that 1) it does not rely on correspondences and 2) it incorporates gradient information via a statistical model instead of a weighting function. Tuning into model-based deformable registration and surface reconstruction, our method solves the problem using the maximum penalized likelihood estimation. Unlike almost all other methods that handle the registration and deformation separately and optimized them sequentially, our method optimizes them simultaneously. The framework was evaluated in two example clinical applications and a simulation study for point-based, contour-based and surface reconstruction, respectively. Experiments showed its sub-degree and sub-millimeter registration accuracy and superiority to the state-of-the-art methods. It is expected that our algorithms, when thoroughly validated, can be used as valuable tools for image-guided interventions.
published_or_final_version
Orthopaedics and Traumatology
Doctoral
Doctor of Philosophy

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Gibson, Christopher John. "Three dimensional display of tomographic images using shaded surfaces." Thesis, Durham University, 1988. http://etheses.dur.ac.uk/6436/.

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Several medical imaging techniques are capable of producing tomographic images, corresponding to cross-sections through the body. A stack of adjacent sections contains three dimensional information about the organs of interest, and this can be presented on a two dimensional screen using shaded surface techniques. In order to facilitate the routine use of such images, algorithms and techniques were developed on a conventional medical imaging computer system in a hospital environment. Several object representation schemes were compared, and two new schemes were devised. The 'solid binary object' technique facilitated exploration of the interior of an object, while the 'ordered surface list' technique enabled real time display of object surfaces. Several shading algorithms were compared, and a local polynomial fitting routine was devised. This was found to be superior to other methods using objective evaluation of the accuracy of surface normal estimations, and subjective evaluation of the corresponding image appearance. The techniques developed were applied to a variety of data obtained using xray computed tomography, nuclear magnetic resonance and emission computed tomography. For display of myocardial tomograms, a technique was devised for superposition of colour coded coronary arteries, showing their relationship to observed perfusion defects. For display of time varying images of the heart, a rapid display routine was developed to enable ventricular wall motion to be evaluated from any angle. Colour display techniques were also applied to this data to produce single images which incorporated kinetic as well as morphological information. The results obtained have confirmed that shaded surface images can be produced using computers currently available in hospital imaging departments. Interactive object modification and real time object display can be achieved without requiring special hardware.

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Narayanan, Sreeram. "Rapid 3d seed reconstruction from incomplete data sets for image guided prostate brachytherapy /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/5825.

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Fan, Li. "3D reconstruction and deformation analysis from medical image sequences with applications in left ventricle and lung /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9999280.

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Quartararo, John David. "Semi-automated segmentation of 3D medical ultrasound images." Worcester, Mass. : Worcester Polytechnic Institute, 2008. http://www.wpi.edu/Pubs/ETD/Available/etd-020509-161314/.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: 3d ultrasound; ultrasound; image processing; image segmentation; 3d image segmentation; medical imaging Includes bibliographical references (p.142-148).

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關福延 and Folk-year Kwan. "An intelligent approach to automatic medical model reconstruction fromserial planar CT images." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31243216.

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Books on the topic "Three-dimensional image in medicine"

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Jean-Louis, Amans, ed. Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. Dordrecht: Springer Netherlands, 1996.

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Grangeat, Pierre, and Jean-Louis Amans, eds. Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8749-5.

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Westermann, Birgit. Parallel volume rendering for image-guided surgery. Aachen: Shaker Verlag, 1998.

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Medical, Data International Inc. Image-guided interventions. Santa Ana, Calif: Medical Data International, 1999.

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1937-, Höhne K. H., Fuchs Henry 1948-, Pizer Stephen M, and North Atlantic Organization. Scientific Affairs Division., eds. 3D imaging in medicine: Algorithms, systems, applications. Berlin: Springer-Verlag, 1990.

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Wong, Kenneth H. Medical imaging 2010: Visualization, image-guided procedures, and modeling : 14-16 February 2010, San Diego, California, United States. Bellingham, Wash: SPIE, 2010.

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Wong, Kenneth H. Medical imaging 2010: Visualization, image-guided procedures, and modeling : 14-16 February 2010, San Diego, California, United States. Bellingham, Wash: SPIE, 2010.

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Wong, Kenneth H., and Michael I. Miga. Medical imaging 2009: Visualization, image-guided procedures, and modeling : 8-10 February 2009, Lake Buena Vista, Florida, United States. Edited by SPIE (Society) and American Association of Physicists in Medicine. Bellingham, Wash: SPIE, 2009.

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McAleavey, Stephen A. Medical imaging 2009: Ultrasonic imaging and signal processing : 8-9 February 2009, Lake Buena Vista, Florida, United States. Edited by SPIE (Society) and American Association of Physicists in Medicine. Bellingham, Wash: SPIE, 2009.

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McAleavey, Stephen A. Medical imaging 2009: Ultrasonic imaging and signal processing : 8-9 February 2009, Lake Buena Vista, Florida, United States. Edited by SPIE (Society) and American Association of Physicists in Medicine. Bellingham, Wash: SPIE, 2009.

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Book chapters on the topic "Three-dimensional image in medicine"

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Fuchs, Henry. "Systems for Display of Three-Dimensional Medical Image Data." In 3D Imaging in Medicine, 315–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84211-5_21.

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Katayama, Eisaku, Gouki Ohmori, and Norio Baba. "Three-Dimensional Image Analysis of Myosin Head in Function as Captured by Quick-Freeze Deep-Etch Replica Electron Microscopy." In Advances in Experimental Medicine and Biology, 37–45. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4684-6039-1_5.

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Hoehme, Stefan, Adrian Friebel, Seddik Hammad, Dirk Drasdo, and Jan G. Hengstler. "Creation of Three-Dimensional Liver Tissue Models from Experimental Images for Systems Medicine." In Methods in Molecular Biology, 319–62. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6506-9_22.

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Crossett, Ben, Alistair V. G. Edwards, Melanie Y. White, and Stuart J. Cordwell. "Statistical Analysis of Image Data Provided by Two-Dimensional Gel Electrophoresis for Discovery Proteomics." In Methods in Molecular Medicine™, 271–86. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-148-6_15.

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Shirai, Yoshiaki. "Image Input." In Three-Dimensional Computer Vision, 11–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-82429-6_2.

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Morita, Satoru. "Three Dimensional Image Inpainting." In Lecture Notes in Computer Science, 752–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881223_94.

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Shirai, Yoshiaki. "Image Feature Extraction." In Three-Dimensional Computer Vision, 32–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-82429-6_3.

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Shirai, Yoshiaki. "Image Feature Description." In Three-Dimensional Computer Vision, 69–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-82429-6_4.

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Levakhina, Yulia. "Iterative image reconstruction for tomosynthesis." In Three-Dimensional Digital Tomosynthesis, 75–97. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-05697-1_4.

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Levakhina, Yulia. "Backprojected space in image reconstruction." In Three-Dimensional Digital Tomosynthesis, 99–136. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-05697-1_5.

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Conference papers on the topic "Three-dimensional image in medicine"

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Yuan, Nimu, Jian Zhou, and Jinyi Qi. "Low-dose CT image denoising without high-dose reference images." In The Fifteenth International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, edited by Samuel Matej and Scott D. Metzler. SPIE, 2019. http://dx.doi.org/10.1117/12.2533654.

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Yue, Meghan, Jie Tang, Brian E. Nett, Jiang Hsieh, Roy Nilsen, and Jiahua Fan. "Evaluation of image quality of a deep learning image reconstruction algorithm." In The Fifteenth International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, edited by Samuel Matej and Scott D. Metzler. SPIE, 2019. http://dx.doi.org/10.1117/12.2534961.

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Yu, Zhicong, Chuanyong Bai, and Daniel Gagnon. "Double-helix trajectory for image guided radiation therapy: geometry and image reconstruction." In The Fifteenth International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, edited by Samuel Matej and Scott D. Metzler. SPIE, 2019. http://dx.doi.org/10.1117/12.2534383.

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Li, Tiantian, Mengxi Zhang, Wenyuan Qi, Evren Asma, and Jinyi Qi. "Motion correction of respiratory-gated PET image using deep learning based image registration framework." In The Fifteenth International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, edited by Samuel Matej and Scott D. Metzler. SPIE, 2019. http://dx.doi.org/10.1117/12.2534851.

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Galve, Pablo, Jose Manuel Udias, Alejandro Lopez-Montes, and Joaquin L. Herraiz. "Super-iterative image reconstruction in PET." In The Fifteenth International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, edited by Samuel Matej and Scott D. Metzler. SPIE, 2019. http://dx.doi.org/10.1117/12.2534760.

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Krishna, Arjun, and Klaus Mueller. "Medical (CT) image generation with style." In The Fifteenth International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, edited by Samuel Matej and Scott D. Metzler. SPIE, 2019. http://dx.doi.org/10.1117/12.2534903.

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Matsuura, Masakazu, Jian Zhou, Naruomi Akino, and Zhou Yu. "Feature aware deep learning CT image reconstruction." In The Fifteenth International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, edited by Samuel Matej and Scott D. Metzler. SPIE, 2019. http://dx.doi.org/10.1117/12.2534614.

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Sidky, Emil Y., Holly L. Stewart, Christopher E. Kawcak, Wayne McIlwraith, Martine C. Duff, and Xiaochuan Pan. "Bone sparsity model for computed tomography image reconstruction." In The Fifteenth International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, edited by Samuel Matej and Scott D. Metzler. SPIE, 2019. http://dx.doi.org/10.1117/12.2534947.

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Churchill, Victor, and Anne Gelb. "Edge-masked CT image reconstruction from limited data." In The Fifteenth International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, edited by Samuel Matej and Scott D. Metzler. SPIE, 2019. http://dx.doi.org/10.1117/12.2534436.

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Han, Li Long, and Ji Zhou. "Design and implementation of synthesizing three-dimensional animation with real image." In 2012 International Symposium on Information Technology in Medicine and Education (ITME 2012). IEEE, 2012. http://dx.doi.org/10.1109/itime.2012.6291459.

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Reports on the topic "Three-dimensional image in medicine"

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Smith, Douglas R. Three-Dimensional Particle Image Velocimetry System. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada411006.

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Creager, K. C., L A Preston, R. S. Crosson, T. Van Wagoner, and A. M. Tréhu. Three-dimensional reflection image of the subducting Juan de Fuca plate. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/222494.

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Harris, C. L. Digital spall radiograph analysis system: Report on simulated three- dimensional digital spall image reconstruction fidelity. Office of Scientific and Technical Information (OSTI), January 1990. http://dx.doi.org/10.2172/434882.

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Yassin Hassan. Full-Volume, Three-Dimensional, Transient Measurements of Bubbly Flows Using Particle Tracking Velocimetry and Shadow Image Velocimetry Coupled with Pattern Recognition Techniques. Office of Scientific and Technical Information (OSTI), November 2001. http://dx.doi.org/10.2172/791466.

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Jarron, Matthew, Amy R. Cameron, and James Gemmill. Dundee Discoveries Past and Present. University of Dundee, November 2020. http://dx.doi.org/10.20933/100001182.

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A series of self-guided walking tours through pioneering scientific research in medicine, biology, forensics, nursing and dentistry from the past to the present. Dundee is now celebrated internationally for its pioneering work in medical sciences, in particular the University of Dundee’s ground-breaking research into cancer, diabetes, drug development and surgical techniques. But the city has many more amazing stories of innovation and discovery in medicine and biology, past and present, and the three walking tours presented here will introduce you to some of the most extraordinary. Basic information about each topic is presented on this map, but you will find more in-depth information, images and videos on the accompanying website at uod.ac.uk/DundeeDiscoveriesMap For younger explorers, we have also included a Scavenger Hunt – look out for the cancer cell symbols on the map and see if you can find the various features listed along the way!

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