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Relevant bibliographies by topics / 3D medical image

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Academic literature on the topic '3D medical image'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "3D medical image"

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Suryakanth, B., and S. A. Hari Prasad. "3D CNN-Residual Neural Network Based Multimodal Medical Image Classification." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 19 (October 31, 2022): 204–14. http://dx.doi.org/10.37394/23208.2022.19.22.

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Multimodal medical imaging has become incredibly common in the area of biomedical imaging. Medical image classification has been used to extract useful data from multimodality medical image data. Magnetic resonance imaging (MRI) and Computed tomography (CT) are some of the imaging methods. Different imaging technologies provide different imaging information for the same part. Traditional ways of illness classification are effective, but in today's environment, 3D images are used to identify diseases. In comparison to 1D and 2D images, 3D images have a very clear vision. The proposed method use

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Abdulkadhim Hameedi, Balsam, Muna Majeed Laftah, and Anwar Abbas Hattab. "Data Hiding in 3D-Medical Image." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 03 (2022): 72–88. http://dx.doi.org/10.3991/ijoe.v18i03.28007.

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Information hiding strategies have recently gained popularity in a variety of fields. Digital audio, video, and images are increasingly being labelled with distinct but undetectable marks that may contain a hidden copyright notice or serial number, or even directly help to prevent unauthorized duplication. This approach is extended to medical images by hiding secret information in them using the structure of a different file format. The hidden information may be related to the patient. In this paper, a method for hiding secret information in DICOM images is proposed based on Discrete Wavelet T

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Hanry, Ham, Wesley Julian, and Hendra. "Computer vision based 3D reconstruction : A review." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (2019): 2394–402. https://doi.org/10.11591/ijece.v9i4.pp2394-2402.

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3D reconstruction are used in many fields starts from the object reconstruction such as site, cultural artifacts in both ground and under the sea levels, medical imaging data, nuclear substantional. The scientist are beneficial for these task in order to learn, keep and better visual enhancement into 3D data. In this paper we differentiate the algorithm used depends on the input image: single still image, RGB-Depth image, multiperspective of 2D images, and video sequences. The prior works also explained how the 3D reconstruction perform in many fields and using various algorithms.

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said, Shaimaa Ahmed El. "3D medical image segmentation technique." International Journal of Biomedical Engineering and Technology 17, no. 3 (2015): 232. http://dx.doi.org/10.1504/ijbet.2015.068108.

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Ban, Yuxi, Yang Wang, Shan Liu, et al. "2D/3D Multimode Medical Image Alignment Based on Spatial Histograms." Applied Sciences 12, no. 16 (2022): 8261. http://dx.doi.org/10.3390/app12168261.

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The key to image-guided surgery (IGS) technology is to find the transformation relationship between preoperative 3D images and intraoperative 2D images, namely, 2D/3D image registration. A feature-based 2D/3D medical image registration algorithm is investigated in this study. We use a two-dimensional weighted spatial histogram of gradient directions to extract statistical features, overcome the algorithm’s limitations, and expand the applicable scenarios under the premise of ensuring accuracy. The proposed algorithm was tested on CT and synthetic X-ray images, and compared with existing algori

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Shah, Said Khalid. "Nonrigid Medical Image Registration Based on Curves." International Journal of Image and Graphics 17, no. 02 (2017): 1750011. http://dx.doi.org/10.1142/s0219467817500115.

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Medical image registration is the process of aligning two or more images in such a way that its anatomical structures properly overlap each other in a common spatial domain and resultant 3D images can be used for diagnosis and therapy by surgeons. A number of nonlinear methods have been developed for inter-subject and intra-subject 3D medical image registration. This paper is a part of research experiments which uses the Fast Radial Basis Function (RBF) technique for nonrigid registration of 3D medical images. The technique is a point-based registration evaluation algorithm which registers MR

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Wang, Siwen, Churan Wang, Fei Gao, et al. "Autoregressive Sequence Modeling for 3D Medical Image Representation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 8 (2025): 7871–79. https://doi.org/10.1609/aaai.v39i8.32848.

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Three-dimensional (3D) medical images, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), are essential for clinical applications. However, the need for diverse and comprehensive representations is particularly pronounced when considering the variability across different organs, diagnostic tasks, and imaging modalities. How to effectively interpret the intricate contextual information and extract meaningful insights from these images remains an open challenge to the community. While current self-supervised learning methods have shown potential, they often consider an image

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Soh, Jung, Mei Xiao, Thao Do, Oscar Meruvia-Pastor, and Christoph W. Sensen. "Integrative Visualization of Temporally Varying Medical Image Patterns." Journal of Integrative Bioinformatics 8, no. 2 (2011): 75–84. http://dx.doi.org/10.1515/jib-2011-161.

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Summary We have developed a tool for the visualization of temporal changes of disease patterns, using stacks of medical images collected in time-series experiments. With this tool, users can generate 3D surface models representing disease patterns and observe changes over time in size, shape, and location of clinically significant image patterns. Statistical measurements of the volume of the observed disease patterns can be performed simultaneously. Spatial data integration occurs through the combination of 2D slices of an image stack into a 3D surface model. Temporal integration occurs throug

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Wang, Chuin-Mu, Chieh-Ling Huang, and Sheng-Chih Yang. "3D Shape-Weighted Level Set Method for Breast MRI 3D Tumor Segmentation." Journal of Healthcare Engineering 2018 (June 13, 2018): 1–15. http://dx.doi.org/10.1155/2018/7097498.

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Three-dimensional (3D) medical image segmentation is used to segment the target (a lesion or an organ) in 3D medical images. Through this process, 3D target information is obtained; hence, this technology is an important auxiliary tool for medical diagnosis. Although some methods have proved to be successful for two-dimensional (2D) image segmentation, their direct use in the 3D case has been unsatisfactory. To obtain more precise tumor segmentation results from 3D MR images, in this paper, we propose a method known as the 3D shape-weighted level set method (3D-SLSM). The proposed method first

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Zerva, Matina Ch, Michalis Vrigkas, Lisimachos P. Kondi, and Christophoros Nikou. "Improving 3D Medical Image Compression Efficiency Using Spatiotemporal Coherence." Electronic Imaging 2020, no. 10 (2020): 63–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.10.ipas-063.

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Advanced methodologies for transmitting compressed images, within acceptable ranges of transmission rate and loss of information, make it possible to transmit a medical image through a communication channel. Most prior works on 3D medical image compression consider volumetric images as a whole but fail to account for the spatial and temporal coherence of adjacent slices. In this paper, we set out to develop a 3D medical image compression method that extends the 3D wavelet difference reduction algorithm by computing the similarity of the pixels in adjacent slices and progressively compress only

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Dissertations / Theses on the topic "3D medical image"

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Studholme, Colin. "Measures of 3D medical image alignment." Thesis, King's College London (University of London), 1997. https://kclpure.kcl.ac.uk/portal/en/theses/measures-of-3d-medical-image-alignment(7e3dd0a9-6dc2-4ff0-8b9f-8fd513728ffb).html.

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Dorgham, Osama. "High speed 2D/3D medical image registration." Thesis, University of East Anglia, 2010. https://ueaeprints.uea.ac.uk/32227/.

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Afandi, Ahmad. "Efficient reconfigurable architectures for 3D medical image compression." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/7677.

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Recently, the more widespread use of three-dimensional (3-D) imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US) have generated a massive amount of volumetric data. These have provided an impetus to the development of other applications, in particular telemedicine and teleradiology. In these fields, medical image compression is important since both efficient storage and transmission of data through high-bandwidth digital communication lines are of crucial importance. Despite their advantages, most 3-D m

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Adolfsson, Karin. "Visual Evaluation of 3D Image Enhancement." Thesis, Linköping University, Department of Biomedical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7944.

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<p>Technologies in image acquisition have developed and often provide image volumes in more than two dimensions. Computer tomography and magnet resonance imaging provide image volumes in three spatial dimensions. The image enhancement methods have developed as well and in this thesis work 3D image enhancement with filter networks is evaluated.</p><p>The aims of this work are; to find a method which makes the initial parameter settings in the 3D image enhancement processing easier, to compare 2D and 3D processed image volumes visualized with different visualization techniques and to give an ill

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Quartararo, John David. "Semi-Automated Segmentation of 3D Medical Ultrasound Images." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/155.

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A level set-based segmentation procedure has been implemented to identify target object boundaries from 3D medical ultrasound images. Several test images (simulated, scanned phantoms, clinical) were subjected to various preprocessing methods and segmented. Two metrics of segmentation accuracy were used to compare the segmentation results to ground truth models and determine which preprocessing methods resulted in the best segmentations. It was found that by using an anisotropic diffusion filtering method to reduce speckle type noise with a 3D active contour segmentation routine using the leve

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Estienne, Théo. "Deep learning-based methods for 3D medical image registration." Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG055.

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Cette thèse se concentre sur des nouvelles approches d'apprentissage profond (aussi appelé deep learning) pour trouver le meilleur déplacement entre deux images médicales différentes. Ce domaine de recherche, appelé recalage d'images, a de nombreuses applications dans la prise en charge clinique, notamment la fusion de différents types d'imagerie ou le suivi temporel d'un patient. Ce domaine est étudié depuis de nombreuses années avec diverses méthodes, telles que les méthodes basées sur des difféomorphismes, sur des graphes ou sur des équations physiques. Récemment, des méthodes basées sur l'

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ZHANG, KEJUN. "3D MEDICAL IMAGE APPLICATIONS OF A 2D CONSTRAINED DEFORMABLE CONTOUR METHOD." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1092408492.

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Al, Zu'bi Shadi Mahmoud. "3D multiresolution statistical approaches for accelerated medical image and volume segmentation." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5300.

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Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical model

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Grandi, Jerônimo Gustavo. "Multidimensional similarity search for 2D-3D medical data correlation and fusion." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/104133.

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Imagens da anatomia interna são essenciais para as práticas médicas. Estabelecer correlação entre elas, é um importante procedimento para diagnóstico e tratamento. Nessa dissertação, é proposta uma abordagem para correlacionar dados multidimensionais de mesma modalidade de aquisição baseando-se somente nas informações de intensidade de pixels e voxels. O trabalho foi dividido em duas fases de implementação. Na primeira, foi explorado o problema de similaridade entre imagens médicas usando a perspectiva de análise de qualidade de imagem. Isso levou ao desenvolvimento de uma técnica de dois pass

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Poon, Miranda. "3D livewire and live-vessel : minimal path methods for interactive medical image segmentation." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2736.

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Medical image analysis is a ubiquitous and essential part of modem health care. A crucial first step to this is segmentation, which is often complicated by many factors including subject diversity, pathology, noise corruption, and poor image resolution. Traditionally, manual tracing by experts was done. While considered accurate, this process is time consuming and tedious, especially when performed slice-by-slice on three-dimensional (3D) images over large datasets or on two-dimensional (2D) but topologically complicated images such as a retinography. On the other hand, fully-automated methods

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Books on the topic "3D medical image"

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Caramella, Davide. 3D Image Processing: Techniques and Clinical Applications. Springer Berlin Heidelberg, 2002.

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Kumar, Sandeep, Shilpa Rani, and K. Ramya Laxmi. Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing. Edited by Rohit Raja. CRC Press, 2020. http://dx.doi.org/10.1201/9780429354526.

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

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Argyriou, Vasileios. Image, video & 3D data registration: Medical, satellite and video processing applications with quality metrics. Wiley, 2015.

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Höhne, Karl Heinz. 3D Imaging in Medicine: Algorithms, Systems, Applications. Springer Berlin Heidelberg, 1990.

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Jähne, Bernd. Digital image processing: Concepts, algorithms, and scientific applications. Springer-Verlag, 1991.

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Jähne, Bernd. Digital image processing: Concepts, algorithms, and scientific applications. 3rd ed. Springer-Verlag, 1995.

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Jähne, Bernd. Digital image processing: Concepts, algorithms, and scientific applications. 2nd ed. Springer-Verlag, 1993.

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Sinclair, Paul. Display techniques for 3D medical images. University ofManchester, Department of Computer Science, 1997.

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Knopp, Tobias. Magnetic Particle Imaging: An Introduction to Imaging Principles and Scanner Instrumentation. Springer Berlin Heidelberg, 2012.

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Book chapters on the topic "3D medical image"

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Tian, Zhen, and Xiaofeng Yang. "2D–3D Transformation for 3D Volumetric Imaging." In Medical Image Synthesis. CRC Press, 2023. http://dx.doi.org/10.1201/9781003243458-13.

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Zeng, Gengsheng Lawrence. "3D Image Reconstruction." In Medical Image Reconstruction. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-05368-9_5.

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John, Nigel W. "Basis and Principles of Virtual Reality in Medical Imaging." In 3D Image Processing. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-59438-0_25.

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Becker, Matthias, and Nadia Magnenat-Thalmann. "Deformable Models in Medical Image Segmentation." In 3D Multiscale Physiological Human. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-6275-9_4.

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Verbeek, Fons J., and D. P. Huijsmans. "A Graphical Database for 3D Reconstruction Supporting (4) Different Geometrical Representations." In Medical Image Databases. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5553-7_5.

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Banik, Shantanu, Rangaraj M. Rangayyan, and Graham S. Boag. "Introduction to Medical Image Analysis." In Landmarking and Segmentation of 3D CT Images. Springer International Publishing, 2009. http://dx.doi.org/10.1007/978-3-031-01635-6_1.

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George, Yasmeen, Bhavna Antony, Hiroshi Ishikawa, Gadi Wollstein, Joel Schuman, and Rahil Garnavi. "3D-CNN for Glaucoma Detection Using Optical Coherence Tomography." In Ophthalmic Medical Image Analysis. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32956-3_7.

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Sanches, João, Jorge S. Marques, Fausto Pinto, and Paulo J. Ferreira. "A 3D Ultrasound System for Medical Diagnosis." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_103.

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

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Sanchez-Ortiz, Gerardo I., Jérôme Declerck, Miguel Mulet-Parada, and J. Alison Noble. "Automating 3D Echocardiographic Image Analysis." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-40899-4_71.

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Conference papers on the topic "3D medical image"

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Shao, Xinyuan, Yiqing Shen, and Mathias Unberath. "Memorizing SAM: 3D medical Segment Anything Model with memorizing transformer." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3047046.

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Singh, Apoorva, G. R. Hemalakshmi, Bhavya Lalchandani, Ishank Singhal, Akshat Gupta, and Nilanjan Sarkar. "3D Medical Image Modeling utilizing DICOM Data." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10984777.

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Lumetti, Luca, Vittorio Pipoli, Kevin Marchesini, Elisa Ficarra, Costantino Grana, and Federico Bolelli. "Accurate 3D Medical Image Segmentation with Mambas." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10981167.

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Makandar, Aziz, and Rekha Biradar. "Tucker Decomposition Based Lossless Image Compression for 3D Medical Image." In 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2024. https://doi.org/10.1109/csitss64042.2024.10816987.

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Vester-Christensen, Martin, Søren G. Erbou, Sune Darkner, and Rasmus Larsen. "Accelerated 3D image registration." In Medical Imaging, edited by Josien P. W. Pluim and Joseph M. Reinhardt. SPIE, 2007. http://dx.doi.org/10.1117/12.709373.

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Marquez, Alejandra, and Alex Cuadros. "3D Medical Image Segmentation based on 3D Convolutional Neural Networks." In LatinX in AI at Neural Information Processing Systems Conference 2018. Journal of LatinX in AI Research, 2018. http://dx.doi.org/10.52591/lxai201812031.

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A neural network is a mathematical model that is able to perform a task automatically or semi-automatically after learning the human knowledge that we provided. Moreover, a Convolutional Neural Network (CNN) is a type of sophisticated neural network that has shown to efficiently learn tasks related to the area of image analysis (among other areas). One example of these tasks is image segmentation, which aims to find regions or separable objects within an image. A more specific type of segmentation called semantic segmentation, makes sure that each region has a semantic meaning by giving it a l

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West, Jay B., and Calvin R. Maurer, Jr. "A system for finding a 3D target without a 3D image." In Medical Imaging, edited by Michael I. Miga and Kevin R. Cleary. SPIE, 2008. http://dx.doi.org/10.1117/12.771460.

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Wang, Shyh-Roei, Yung-Nien Sun, Fong-Ming Chang, and Ching-Fen Jiang. "3D image display of fetal ultrasonic images by thin shell." In Medical Imaging '99, edited by Kenneth M. Hanson. SPIE, 1999. http://dx.doi.org/10.1117/12.348548.

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Romdhane, Feriel, Faouzi Benzarti, and 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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Baskurt, Atilla M., Hugues Benoit-Cattin, and Christophe Odet. "3D medical image coding method using a separable 3D wavelet transform." In Medical Imaging 1995, edited by Yongmin Kim. SPIE, 1995. http://dx.doi.org/10.1117/12.207611.

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Reports on the topic "3D medical image"

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Martin, Kathi, Nick Jushchyshyn, and Claire King. James Galanos, Silk Chiffon Afternoon Dress c. Fall 1976. Drexel Digital Museum, 2018. http://dx.doi.org/10.17918/q3g5-n257.

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The URL links to a website page in the Drexel Digital Museum (DDM) fashion image archive containing a 3D interactive panorama of an evening suit by American fashion designer James Galanos with related text. This afternoon dress is from Galanos' Fall 1976 collection. It is made from pale pink silk chiffon and finished with hand stitching on the hems and edges of this dress, The dress was gifted to Drexel University as part of The James G. Galanos Archive at Drexel University in 2016. After it was imaged the gown was deemed too fragile to exhibit. By imaging it using high resolution GigaPan tech

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Hammouti, A., S. Larmagnat, C. Rivard, and D. Pham Van Bang. Use of CT-scan images to build geomaterial 3D pore network representation in preparation for numerical simulations of fluid flow and heat transfer, Quebec. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331502.

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Non-intrusive techniques such as medical CT-Scan or micro-CT allow the definition of 3D connected pore networks in porous materials, such as sedimentary rocks or concrete. The definition of these networks is a key step towards the evaluation of fluid flow and heat transfer in energy resource (e.g., hydrocarbon and geothermal reservoirs) and CO2 sequestration research projects. As material heterogeneities play a role at all scales (from micro- to project-scale), numerical models represent a powerful tool for bridging the gap between small-scale measurements provided by X-ray imaging techniques

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Martin, Kathi, Nick Jushchyshyn, and Claire King. James Galanos Evening Gown c. 1957. Drexel Digital Museum, 2018. http://dx.doi.org/10.17918/jkyh-1b56.

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The URL links to a website page in the Drexel Digital Museum (DDM) fashion image archive containing a 3D interactive panorama of an evening suit by American fashion designer James Galanos with related text. This evening gown is from Galanos' Fall 1957 collection. It is embellished with polychrome glass beads in a red and green tartan plaid pattern on a base of silk . It was a gift of Mrs. John Thouron and is in The James G. Galanos Archive at Drexel University. The panorama is an HTML5 formatted version of an ultra-high resolution ObjectVR created from stitched tiles captured with GigaPan tech

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Martin, Kathi, Nick Jushchyshyn, and Claire King. James Galanos, Wool Evening Suit. Fall 1984. Drexel Digital Museum, 2018. http://dx.doi.org/10.17918/6gzv-pb45.

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The URL links to a website page in the Drexel Digital Museum (DDM) fashion image archive containing a 3D interactive panorama of an evening suit by American fashion designer James Galanos with related text. This evening suit is from Galanos Fall 1984 collection. The skirt and bodice of the jacket are black and white plaid wool. The jacket sleeves are black mink with leather inserts that contrast the sheen of the leather against the luster of the mink and reduce some of the bulk of the sleeve. The suit is part of The James G. Galanos Archive at Drexel University gifted to Drexel University in 2

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Martin, Kathi, Nick Jushchyshyn, and Claire King. Christian Lacroix Evening gown c.1990. Drexel Digital Museum, 2017. http://dx.doi.org/10.17918/wq7d-mc48.

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The URL links to a website page in the Drexel Digital Museum (DDM) fashion image archive containing a 3D interactive panorama of an evening gown by French fashion designer Christian Lacroix with related text. This evening gown by Christian Lacroix is from his Fall 1990 collection. It is constructed from silk plain weave, printed with an abstract motif in the bright, deep colors of the local costumes of Lacroix's native Arles, France; and embellished with diamanté and insets of handkerchief edged silk chiffon. Ruffles of pleated silk organza in a neutral bird feather print and also finished wit

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