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Journal articles on the topic '3D medical image'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

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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Kang, Dongwoo, Jin-Ho Choi, and Hyoseok Hwang. "Autostereoscopic 3D Display System for 3D Medical Images." Applied Sciences 12, no. 9 (2022): 4288. http://dx.doi.org/10.3390/app12094288.

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Recent advances in autostereoscopic three-dimensional (3D) display systems have led to innovations in consumer electronics and vehicle systems (e.g., head-up displays). However, medical images with stereoscopic depth provided by 3D displays have yet to be developed sufficiently for widespread adoption in diagnostics. Indeed, many stereoscopic 3D displays necessitate special 3D glasses that are unsuitable for clinical environments. This paper proposes a novel glasses-free 3D autostereoscopic display system based on an eye tracking algorithm and explores its viability as a 3D navigator for cardi

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Dong, Shidu, Zhi Liu, Huaqiu Wang, Yihao Zhang, and Shaoguo Cui. "A Separate 3D Convolutional Neural Network Architecture for 3D Medical Image Semantic Segmentation." Journal of Medical Imaging and Health Informatics 9, no. 8 (2019): 1705–16. http://dx.doi.org/10.1166/jmihi.2019.2797.

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To exploit three-dimensional (3D) context information and improve 3D medical image semantic segmentation, we propose a separate 3D (S3D) convolution neural network (CNN) architecture. First, a two-dimensional (2D) CNN is used to extract the 2D features of each slice in the xy-plane of 3D medical images. Second, one-dimensional (1D) features reassembled from the 2D features in the z-axis are input into a 1D-CNN and are then classified feature-wise. Analysis shows that S3D-CNN has lower time complexity, fewer parameters and less memory space requirements than other 3D-CNNs with a similar structu

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Gunasekaran, Ganesan, and Meenakshisundaram Venkatesan. "An Efficient Technique for Three-Dimensional Image Visualization Through Two-Dimensional Images for Medical Data." Journal of Intelligent Systems 29, no. 1 (2017): 100–109. http://dx.doi.org/10.1515/jisys-2017-0315.

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Abstract The main idea behind this work is to present three-dimensional (3D) image visualization through two-dimensional (2D) images that comprise various images. 3D image visualization is one of the essential methods for excerpting data from given pieces. The main goal of this work is to figure out the outlines of the given 3D geometric primitives in each part, and then integrate these outlines or frames to reconstruct 3D geometric primitives. The proposed technique is very useful and can be applied to many kinds of images. The experimental results showed a very good determination of the reco

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Luo, Xiujuan. "Three-Dimensional Image Quality Evaluation and Optimization Based on Convolutional Neural Network." Traitement du Signal 38, no. 4 (2021): 1041–49. http://dx.doi.org/10.18280/ts.380414.

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Currently, three-dimensional (3D) imaging has been successfully applied in medical health, movie viewing, games, and military. To make 3D images more pleasant to the eyes, the accurate judgement of image quality becomes the key step in content preparation, compression, and transmission in 3D imaging. However, there is not yet a satisfactory evaluation method that objectively assesses the quality of 3D images. To solve the problem, this paper explores the evaluation and optimization of 3D image quality based on convolutional neural network (CNN). Specifically, a 3D image quality evaluation mode

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Kim, Man-Bae, Seong-Eun Jang, Woo-Keun Lee, and Chang-Yeol Choi. "3D Stereoscopic Image Generation of a 2D Medical Image." Journal of Broadcast Engineering 15, no. 6 (2010): 723–30. http://dx.doi.org/10.5909/jbe.2010.15.6.723.

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Wang, Chung Shing, Teng Ruey Chang, Wei Hua A. Wang, and Man Ching Lin. "Rapid Prototyping STL Reconstruction for CT Medical Image in Fused Deposition Modelling." Key Engineering Materials 443 (June 2010): 522–27. http://dx.doi.org/10.4028/www.scientific.net/kem.443.522.

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The main objective of this research is to reconstruct 3D rapid prototyping (RP) models for computer tomography (CT) medical images in fused deposition modeling (FDM). It demonstrates a technique to convert medical images to points cloud, and simplify into STL (Stereo-Lithography) triangular meshes for RP machine in fused deposition modelling. The grey prediction algorithm is used to sort contour point data in each layer of the medical image. The contour difference detection operation is used to sequence the points for each layer. The 3D STL meshes are then constructed by layer-by-layer sequenc

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Kurniatie, Menik Dwi, Dhega Ivory Andari, and Talitha Asmaria. "3D Printing of Heart Model as Medical Education Tools." Diffusion Foundations and Materials Applications 33 (April 3, 2023): 85–94. http://dx.doi.org/10.4028/p-l0k8s7.

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Abstract. 3D printing is a rapidly developing technology in the medical world that has been used for pre-operative planning, prosthetic manufacturing, and training for medical education. This 3D printing is needed for medical education to make it easier for students to study anatomical structures. The advantages of 3D printing provide more detail and tactile representation of anatomical aspects of organs to address the problems of online learning and cadaveric limitations. This research aimed to develop the manufacture of 3D printed models of the human heart organ to improve understanding in l

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Islam, Kh Tohidul, Sudanthi Wijewickrema, and Stephen O’Leary. "A rotation and translation invariant method for 3D organ image classification using deep convolutional neural networks." PeerJ Computer Science 5 (March 4, 2019): e181. http://dx.doi.org/10.7717/peerj-cs.181.

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Three-dimensional (3D) medical image classification is useful in applications such as disease diagnosis and content-based medical image retrieval. It is a challenging task due to several reasons. First, image intensity values are vastly different depending on the image modality. Second, intensity values within the same image modality may vary depending on the imaging machine and artifacts may also be introduced in the imaging process. Third, processing 3D data requires high computational power. In recent years, significant research has been conducted in the field of 3D medical image classifica

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Kesavamurthy, T., and Subha Rani. "Dicom Color Medical Image Compression using 3D-SPIHT for Pacs Application." International Journal of Biomedical Science 4, no. 2 (2008): 113–19. http://dx.doi.org/10.59566/ijbs.2008.4113.

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The proposed algorithm presents an application of 3D-SPIHT algorithm to color volumetric dicom medical images using 3D wavelet decomposition and a 3D spatial dependence tree. The wavelet decomposition is accomplished with biorthogonal 9/7 filters. 3D-SPIHT is the modern-day benchmark for three dimensional image compressions. The three-dimensional coding is based on the observation that the sequences of images are contiguous in the temporal axis and there is no motion between slices. Therefore, the 3D discrete wavelet transform can fully exploit the inter-slices correlations. The set partitioni

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Li, Chen, Wei Chen, and Yusong Tan. "Point-Sampling Method Based on 3D U-Net Architecture to Reduce the Influence of False Positive and Solve Boundary Blur Problem in 3D CT Image Segmentation." Applied Sciences 10, no. 19 (2020): 6838. http://dx.doi.org/10.3390/app10196838.

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Malignant lesions are a huge threat to human health and have a high mortality rate. Locating the contour of organs is a preparation step, and it helps doctors diagnose correctly. Therefore, there is an urgent clinical need for a segmentation model specifically designed for medical imaging. However, most current medical image segmentation models directly migrate from natural image segmentation models, thus ignoring some characteristic features for medical images, such as false positive phenomena and the blurred boundary problem in 3D volume data. The research on organ segmentation models for me

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Yang, Deshan, Jie Zheng, Ahmad Nofal, Joseph Deasy, and Issam M. El Naqa. "Techniques and software tool for 3D multimodality medical image segmentation." Journal of Radiation Oncology Informatics 1, no. 1 (2017): 1–22. http://dx.doi.org/10.5166/jroi-1-1-4.

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The era of noninvasive diagnostic radiology and image-guided radiotherapy has witnessed burgeoning interest in applying different imaging modalities to stage and localize complex diseases such as atherosclerosis or cancer. It has been observed that using complementary information from multimodality images often significantly improves the robustness and accuracy of target volume definitions in radiotherapy treatment of cancer. In this work, we present techniques and an interactive software tool to support this new framework for 3D multimodality medical image segmentation. To demonstrate this me

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Xiao, Qianmu, and Liang Zhao. "End-to-End 3D Liver CT Image Synthesis from Vasculature Using a Multi-Task Conditional Generative Adversarial Network." Applied Sciences 13, no. 11 (2023): 6784. http://dx.doi.org/10.3390/app13116784.

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Acquiring relevant, high-quality, and heterogeneous medical images is essential in various types of automated analysis, used for a variety of downstream data augmentation tasks. However, a large number of real image samples are expensive to obtain, especially for 3D medical images. Therefore, there is an urgent need to synthesize realistic 3D medical images. However, the existing generator models have poor stability and lack the guidance of prior medical knowledge. To this end, we propose a multi-task (i.e., segmentation task and generation task) 3D generative adversarial network (GAN) for the

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Hu, Zhan Li, Jian Bao Gui, Jing Zou, et al. "Real-Time 3D Space Coordinate Acquisition of Medical Visualization Data." Applied Mechanics and Materials 44-47 (December 2010): 3534–37. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3534.

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Medical visualization refers to the techniques and processes used to create images of the human body for clinical purposes or medical science including the study of normal anatomy and physiology. The visualization of medical images data sets is to reconstruct 3D images with the 2D slice images so as to reveal the 3D configuration of organs through human visual system. Visual C++ are used to reconstruct 3D images using the CT slice sequence. The key algorithms and human CT 3D visualization results are given in this paper. The coordinates can be acquired by the mouse clicking in the 3D space, by

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Zhang, Hang, Jinwei Zhang, Rongguang Wang, et al. "Efficient Folded Attention for Medical Image Reconstruction and Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (2021): 10868–76. http://dx.doi.org/10.1609/aaai.v35i12.17298.

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Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based on deep neural networks have been developed with promising results, and attention mechanism has been further designed for performance enhancement. However, the large size of 3D volume images poses a great computational challenge to traditional attention methods. In this paper, we propose a folded attention (FA) approach to improve the computational efficiency of traditional attention methods on 3D medical images. The main idea is that we apply tensor folding and unfolding operations to construct four small sub-affinit

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Wang, Shou Quan, Wei Feng, and Wei Bo Guo. "A Survey on 3D Medical Image Visualization." Advanced Materials Research 546-547 (July 2012): 416–19. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.416.

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This paper puts forward a survey on the existing 3D medical image visualization methods. These methods are classified into two groups and typical algorithms in each group are described and analyzed. At last future research directions are discussed.

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Yonker, Shea B., Oleksandr O. Korshak, Timothy Hedstrom, Alexander Wu, Siddharth Atre, and Jürgen P. Schulze. "3D Medical Image Segmentation in Virtual Reality." Electronic Imaging 2019, no. 2 (2019): 188–1. http://dx.doi.org/10.2352/issn.2470-1173.2019.2.ervr-188.

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Arya, Manish, William Cody, Christos Faloutsos, Joel Richardson, and Arthur Toga. "A 3D medical image database management system." Computerized Medical Imaging and Graphics 20, no. 4 (1996): 269–84. http://dx.doi.org/10.1016/s0895-6111(96)00019-5.

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Dai, Yakang, Jian Zheng, Yuetao Yang, Duojie Kuai, and Xiaodong Yang. "Volume-Rendering-Based Interactive 3D Measurement for Quantitative Analysis of 3D Medical Images." Computational and Mathematical Methods in Medicine 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/804573.

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3D medical images are widely used to assist diagnosis and surgical planning in clinical applications, where quantitative measurement of interesting objects in the image is of great importance. Volume rendering is widely used for qualitative visualization of 3D medical images. In this paper, we introduce a volume-rendering-based interactive 3D measurement framework for quantitative analysis of 3D medical images. In the framework, 3D widgets and volume clipping are integrated with volume rendering. Specifically, 3D plane widgets are manipulated to clip the volume to expose interesting objects. 3

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Xue, Jiawen, Li Yin, Zehua Lan, et al. "3D DCT Based Image Compression Method for the Medical Endoscopic Application." Sensors 21, no. 5 (2021): 1817. http://dx.doi.org/10.3390/s21051817.

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This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the qu

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KOBASHI, SYOJI, NAOTAKE KAMIURA, YUTAKA HATA, and FUJIO MIYAWAKI. "FUZZY INFORMATION GRANULATION ON BLOOD VESSEL EXTRACTION FROM 3D TOF MRA IMAGE." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 04 (2000): 409–25. http://dx.doi.org/10.1142/s0218001400000271.

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This paper shows an application of fuzzy information granulation (fuzzy IG) to medical image segmentation. Fuzzy IG is to derive fuzzy granules from information. In the case of medical image segmentation, information and fuzzy granules correspond to an image taken from a medical scanner, and anatomical parts, namely region of interests (ROIs), respectively. The proposed method to granulate information is composed of volume quantization and fuzzy merging. Volume quantization is to gather similar neighboring voxels. The generated quanta are selectively merged according to degrees for pre-defined

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Eom, Junseong, and Sangjun Moon. "Three-Dimensional High-Resolution Digital Inline Hologram Reconstruction with a Volumetric Deconvolution Method." Sensors 18, no. 9 (2018): 2918. http://dx.doi.org/10.3390/s18092918.

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The digital in-line holographic microscope (DIHM) was developed for a 2D imaging technology and has recently been adapted to 3D imaging methods, providing new approaches to obtaining volumetric images with both a high resolution and wide field-of-view (FOV), which allows the physical limitations to be overcome. However, during the sectioning process of 3D image generation, the out-of-focus image of the object becomes a significant impediment to obtaining evident 3D features in the 2D sectioning plane of a thick biological sample. Based on phase retrieved high-resolution holographic imaging and

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Gao, Xiaohong W., Yu Qian, and Rui Hui. "The State of the Art of Medical Imaging Technology: from Creation to Archive and Back." Open Medical Informatics Journal 5, no. 1 (2011): 73–85. http://dx.doi.org/10.2174/1874431101105010073.

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Medical imaging has learnt itself well into modern medicine and revolutionized medical industry in the last 30 years. Stemming from the discovery of X-ray by Nobel laureate Wilhelm Roentgen, radiology was born, leading to the creation of large quantities of digital images as opposed to film-based medium. While this rich supply of images provides immeasurable information that would otherwise not be possible to obtain, medical images pose great challenges in archiving them safe from corrupted, lost and misuse, retrievable from databases of huge sizes with varying forms of metadata, and reusable

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Al-Khuzaie, Maryam I. Mousa, and Waleed A. Mahmoud Al-Jawher. "Enhancing Brain Tumor Classification with a Novel Three-Dimensional Convolutional Neural Network (3D-CNN) Fusion Model." Journal Port Science Research 7, no. 3 (2024): 254–67. http://dx.doi.org/10.36371/port.2024.3.5.

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Three-dimensional convolutional neural networks (3D CNNs) have been widely applied to analyze brain tumour images (BT) to understand the disease's progress better. It is well-known that training 3D-CNN is computationally expensive and has the potential of overfitting due to the small sample size available in the medical imaging field. Here, we proposed a novel 2D-3D approach by converting a 2D brain image to a 3D fused image using a gradient of the image Learnable Weighted. By the 2D-to-3D conversion, the proposed model can easily forward the fused 3D image through a pre-trained 3D model while

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Yang, Feng, Mingyue Ding, and Xuming Zhang. "Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent Neighborhood Descriptor." Sensors 19, no. 21 (2019): 4675. http://dx.doi.org/10.3390/s19214675.

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The non-rigid multi-modal three-dimensional (3D) medical image registration is highly challenging due to the difficulty in the construction of similarity measure and the solution of non-rigid transformation parameters. A novel structural representation based registration method is proposed to address these problems. Firstly, an improved modality independent neighborhood descriptor (MIND) that is based on the foveated nonlocal self-similarity is designed for the effective structural representations of 3D medical images to transform multi-modal image registration into mono-modal one. The sum of

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Prabha, Navaneeth, Naeema Ziyad, Navya Prasad, Jisha P. Abraham, Pristy Paul T, and Rini T Paul. "Enhanced Medical Analysis: Leveraging 3D Visualization and VR-AR Technology." Journal of Sensor Networks and Data Communications 4, no. 3 (2024): 01–09. https://doi.org/10.33140/jsndc.04.03.03.

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Modern healthcare depends heavily on medical imaging, but traditional 2D images frequently lack depth and detail. This paper introduces a novel approach, that turns 2D medical images, such as X-rays, MRIs, and CT scans, into immersive three-dimensional visualizations using virtual and augmented reality (VR/AR) technology. The process consists of four steps: acquiring DICOM medical data, converting the data into 3D models, applying the rendering modes and slicing planes, and deploying the data in VR/AR environments. Preprocessing methods evaluate and improve the quality of medical image data, w

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Sun, Haoran. "A Review of 3D-2D Registration Methods and Applications based on Medical Images." Highlights in Science, Engineering and Technology 35 (April 11, 2023): 200–224. http://dx.doi.org/10.54097/hset.v35i.7055.

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The registration of preoperative three-dimensional (3D) medical images with intraoperative two-dimensional (2D) data is a key technology for image-guided radiotherapy, minimally invasive surgery, and interventional procedures. In this paper, we review 3D-2D registration methods using computed tomography (CT) and magnetic resonance imaging (MRI) as preoperative 3D images and ultrasound, X-ray, and visible light images as intraoperative 2D images. The 3D-2D registration techniques are classified into intensity-based, structure-based, and gradient-based according to the different registration fea

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Koryciak, Sebastian, Maciej Barszczowski, Agnieszka Dąbrowska-Boruch, and Kazimierz Wiatr. "Medical Visualizer 3D: Hardware Controller for Dmd Module." Image Processing & Communications 19, no. 2-3 (2014): 15–23. http://dx.doi.org/10.1515/ipc-2015-0006.

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Abstract In this paper an implementation of the module responsible for the control of micro-mirror array for later use in projection is described. Existing technologies allow for projections of medical images in Digital Imaging and Communications in Medicine format only in the form of a flat 2D image. The 3D Visualizer will allow to display medical images in three dimensions using its own projection surface. The matrix controlling device has been largely developed on the basis of reverse engineering studies carried out on the functional system based on a driver from Texas Instruments. Driver i

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Qu, Lei, Changfeng Wu, and Liang Zou. "3D Dense Separated Convolution Module for Volumetric Medical Image Analysis." Applied Sciences 10, no. 2 (2020): 485. http://dx.doi.org/10.3390/app10020485.

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With the thriving of deep learning, 3D convolutional neural networks have become a popular choice in volumetric image analysis due to their impressive 3D context mining ability. However, the 3D convolutional kernels will introduce a significant increase in the amount of trainable parameters. Considering the training data are often limited in biomedical tasks, a trade-off has to be made between model size and its representational power. To address this concern, in this paper, we propose a novel 3D Dense Separated Convolution (3D-DSC) module to replace the original 3D convolutional kernels. The

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Wei, Wei, Wei Lin, Liang Liu, and Zhong Qin Hu. "2D-3D Medical Image Registration Based on Ant Colony Algorithm." Applied Mechanics and Materials 462-463 (November 2013): 267–73. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.267.

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Object: To optimize the rigidity registration algorithm between X-ray fluoroscopy and CT, and improve the accuracy of registration. Method: By changing the transmission parameters of the ray tracing, it can obtain the original DRR images and the float DRR image for registration. In trials, it uses ant colony algorithm as the optimized search strategy and Mutual information as the similarity measure. Result: ant colony algorithm and the improved ant colony algorithm compared to the classic Powell algorithm to improve the accuracy of registration about 10% and 20%, achieved good results. Conclus

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Rathore, Gurpreet, and Vijay Dhir. "A comparative approach to image registration methods." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 6, no. 2 (2013): 757–62. http://dx.doi.org/10.24297/ijmit.v6i2.3821.

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Image processing methods are possibly able to visualize objects inside the human body. Efficient image processing methods are useful in medical diagnosis, treatment planning and medical research. Medical images are used for medical diagnosis. These images should be geometrically aligned for better observation. Registration is necessary technique to integrate data taken from different measurements. Image Registration is a process of overlaying two or more images that can taken at different times, using different devices, different viewpoints and from different angles in order to have 2D or 3D p

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Barın, Sezin, Uçman Ergün, and Gür Emre Güraksın. "3D MEDICAL IMAGE SEGMENTATION WITH DEEP LEARNING METHODS." International Journal of 3D Printing Technologies and Digital Industry 9, no. 1 (2025): 73–91. https://doi.org/10.46519/ij3dptdi.1571288.

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With advancements in technology, three-dimensional (3D) medical imaging has become vital in modern medicine, contributing to more accurate diagnosis, treatment planning, and personalized medicine. However, segmenting abdominal organs remains a challenging task due to anatomical variations, limited labeled data, and image noise. This study investigates the impact of deep learning-based architectures and preprocessing techniques on 3D organ segmentation using the publicly available Multi-Atlas Labeling Beyond the Cranial Vault (BTCV) dataset. To achieve this, 3D U-Net, UNETR, and SwinUNETR model

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Ahmad, Bilal, Pål Anders Floor, Ivar Farup, and Casper Find Andersen. "Single-Image-Based 3D Reconstruction of Endoscopic Images." Journal of Imaging 10, no. 4 (2024): 82. http://dx.doi.org/10.3390/jimaging10040082.

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A wireless capsule endoscope (WCE) is a medical device designed for the examination of the human gastrointestinal (GI) tract. Three-dimensional models based on WCE images can assist in diagnostics by effectively detecting pathology. These 3D models provide gastroenterologists with improved visualization, particularly in areas of specific interest. However, the constraints of WCE, such as lack of controllability, and requiring expensive equipment for operation, which is often unavailable, pose significant challenges when it comes to conducting comprehensive experiments aimed at evaluating the q

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González Izard, Santiago, Ramiro Sánchez Torres, Óscar Alonso Plaza, Juan Antonio Juanes Méndez, and Francisco José García-Peñalvo. "Nextmed: Automatic Imaging Segmentation, 3D Reconstruction, and 3D Model Visualization Platform Using Augmented and Virtual Reality." Sensors 20, no. 10 (2020): 2962. http://dx.doi.org/10.3390/s20102962.

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The visualization of medical images with advanced techniques, such as augmented reality and virtual reality, represent a breakthrough for medical professionals. In contrast to more traditional visualization tools lacking 3D capabilities, these systems use the three available dimensions. To visualize medical images in 3D, the anatomical areas of interest must be segmented. Currently, manual segmentation, which is the most commonly used technique, and semi-automatic approaches can be time consuming because a doctor is required, making segmentation for each individual case unfeasible. Using new t

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Meng, Lu. "3D Medical Images Registration Based on GPU Parallel Computing." Applied Mechanics and Materials 241-244 (December 2012): 3010–13. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.3010.

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Real time 3D medical image registration method is key technology of medical image processing, especially in surgical operation navigation. However, current 3D medical image registration methods are time-consuming, which can’t meet the real time requirement of clinical application. To solve this problem, this paper presented a high performance computational method based on CUDA ( Compute Unified Device Architecture), which took full advantage of GPU parallel computing under CUDA architecture combined with image multiple scale and maximum mutual information to make fast registration of three dim

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Raihanah Abdani, Siti, Syed Mohd Zahid Syed Zainal Ariffin, Nursuriati Jamil, and Shafaf Ibrahim. "3D-based Convolutional Neural Networks for Medical Image Segmentation." International journal of electrical and computer engineering systems 16, no. 5 (2025): 347–63. https://doi.org/10.32985/ijeces.16.5.1.

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Medical image segmentation is essential for disease screening and diagnosis, particularly through techniques like anatomical and lesion segmentation that can be used to isolate critical regions of interest. However, manual segmentation is labor-intensive, costly, and susceptible to subjective bias, underscoring the need for automation. Deep learning, particularly convolutional neural networks (CNNs), has significantly advanced segmentation accuracy and efficiency. With the introduction of 3D imaging, research has evolved from 2D CNNs to 3D CNNs, which leverage inter-slice information to improv

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Chang- Gyu Kim. "Development of a Medical Phantom to Evaluate the Function of Low Dose 3D MDCT." Medico Legal Update 20, no. 1 (2020): 2025–30. http://dx.doi.org/10.37506/mlu.v20i1.677.

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Background/Objectives: Low dose radiation-based CT imaging is a technology that dramatically improves the information on borders between similar substances but with difference in density. The findings will serve as basic data in developing a CT phantom dedicated for X-ray phase differential imaging.Method/Statistical Analysis: To evaluate the benefits of a phantom for low dose 3D MDCT, SOMATOM Definition AS? (Siemens, Germany) CT scanner that produces 128 slices of images with one rotation was used. The auto-exposure condition (AEC) was applied as it is frequently used in clinical settings for

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Han, Bao Ru, and Jing Bing Li. "Medical Volume Data Zero-Watermarking Algorithm Using 3D-DCT and Chaotic Neural Network." Applied Mechanics and Materials 401-403 (September 2013): 1561–64. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1561.

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This paper presented a new watermarking algorithm based on 3D-DCT and chaotic neural network in order to protect three-dimensional medical images. The algorithm adopts a wealth of information grayscale image as a watermark and three-dimensional medical image as the original carrier. It utilizes chaos neural network for scrambling and encryption of watermarking image. The embedded watermark has cryptographic security significance. Experimental results show that the algorithm is simple, which is a blind watermarking algorithm; the watermark extraction process does not require the original image.

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Shirly, S., and K. Ramesh. "Review on 2D and 3D MRI Image Segmentation Techniques." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 2 (2019): 150–60. http://dx.doi.org/10.2174/1573405613666171123160609.

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Background: Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics. </P><P> Discussion: Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmen

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Zhang, Kuan, Haoji Hu, Kenneth Philbrick, et al. "SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks." Tomography 8, no. 2 (2022): 905–19. http://dx.doi.org/10.3390/tomography8020073.

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There is a growing demand for high-resolution (HR) medical images for both clinical and research applications. Image quality is inevitably traded off with acquisition time, which in turn impacts patient comfort, examination costs, dose, and motion-induced artifacts. For many image-based tasks, increasing the apparent spatial resolution in the perpendicular plane to produce multi-planar reformats or 3D images is commonly used. Single-image super-resolution (SR) is a promising technique to provide HR images based on deep learning to increase the resolution of a 2D image, but there are few report

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Voronin, Viacheslav, Oxana Balabaeva, Svetlana Tokareva, Evgenii Semenishchev, and Vladimir Dub. "Medical image segmentation using modified active contour method." Serbian Journal of Electrical Engineering 14, no. 3 (2017): 401–13. http://dx.doi.org/10.2298/sjee1703401v.

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Image data is of major practical importance in medical informatics. Accurate segmentation of medical images largely determines the final result of image analysis, which provides significant information for 3D visualization, surgical planning and early detection of diseases. In this paper, a modified segmentation approach based on the active contour method is proposed to extract parts of bones from MRI data sets. The efficiency of the method is verified on real MRI slices. Good results are shown in comparison with existing approaches of segmentation of medical data.

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