A novel surface rendering algorithm for 3D reconstruction of medical images

2009 ◽  
Author(s):  
Feng Qian ◽  
Xiuli Ma ◽  
Wanggen Wan ◽  
Jinglin Zhang
Keyword(s):  
3D Reconstruction ◽  
Medical Images ◽  
Surface Rendering

scholarly journals Nextmed: Automatic Imaging Segmentation, 3D Reconstruction, and 3D Model Visualization Platform Using Augmented and Virtual Reality

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2962 ◽  
Author(s):  
Santiago González Izard ◽  
Ramiro Sánchez Torres ◽  
Óscar Alonso Plaza ◽  
Juan Antonio Juanes Méndez ◽  
Francisco José García-Peñalvo
Keyword(s):  
Virtual Reality ◽  
3D Reconstruction ◽  
Medical Image ◽  
New Technologies ◽  
Medical Images ◽  
3D Visualization ◽  
Automatic Segmentation ◽  
Individual Case ◽  
Medical Professionals ◽  
Anatomical Structures

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 technologies, such as computer vision and artificial intelligence for segmentation algorithms and augmented and virtual reality for visualization techniques implementation, we designed a complete platform to solve this problem and allow medical professionals to work more frequently with anatomical 3D models obtained from medical imaging. As a result, the Nextmed project, due to the different implemented software applications, permits the importation of digital imaging and communication on medicine (dicom) images on a secure cloud platform and the automatic segmentation of certain anatomical structures with new algorithms that improve upon the current research results. A 3D mesh of the segmented structure is then automatically generated that can be printed in 3D or visualized using both augmented and virtual reality, with the designed software systems. The Nextmed project is unique, as it covers the whole process from uploading dicom images to automatic segmentation, 3D reconstruction, 3D visualization, and manipulation using augmented and virtual reality. There are many researches about application of augmented and virtual reality for medical image 3D visualization; however, they are not automated platforms. Although some other anatomical structures can be studied, we focused on one case: a lung study. Analyzing the application of the platform to more than 1000 dicom images and studying the results with medical specialists, we concluded that the installation of this system in hospitals would provide a considerable improvement as a tool for medical image visualization.

Three-dimensional reconstruction based on images from spiral high-resolution computed tomography of the temporal bone: anatomy and clinical application

2005 ◽  
Vol 119 (9) ◽  
pp. 693-698 ◽  
Author(s):  
Beom-Cho Jun ◽  
Sun-Wha Song ◽  
Ju-Eun Cho ◽  
Chan-Soon Park ◽  
Dong-Hee Lee ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
3D Reconstruction ◽  
Temporal Bone ◽  
Three Dimensional ◽  
Image Data ◽  
Computer Assisted ◽  
Surface Rendering ◽  
High Resolution Computed Tomography

The aim of this study was to investigate the usefulness of a three-dimensional (3D) reconstruction of computed tomography (CT) images in determining the anatomy and topographic relationship between various important structures. Using 40 ears from 20 patients with various otological diseases, a 3D reconstruction based on the image data from spiral high-resolution CT was performed by segmentation, volume-rendering and surface-rendering algorithms on a personal computer. The 3D display of the middle and inner ear structures was demonstrated in detail. Computer-assisted measurements, many of which could not be easily measured in vivo, of the reconstructed structures provided accurate anatomic details that improved the surgeon’s understanding of spatial relationships. A 3D reconstruction of temporal bone CT might be useful for education and increasing understanding of the anatomical structures of the temporal bone. However, it will be necessary to confirm the correlation between the 3D reconstructed images and histological sections through a validation study.

An Improved Three-Dimensional Target Octonion Representation

2012 ◽  
Vol 263-266 ◽  
pp. 1614-1618
Author(s):  
Xiang Hua Chen ◽  
Juan Zhou
Keyword(s):  
3D Reconstruction ◽  
Traditional Method ◽  
Medical Images ◽  
Three Dimensional ◽  
Experimental Results ◽  
Three Dimensional Objects ◽  
Traditional Structure ◽  
Large Memory ◽  
Octree Representation

It is an efficient way to represent three-dimensional objects by octree.The traditional structure of pointer -based octree representation has several shortcomings,such as requiring large memory,missing relationship between two nodes,etc.Based on analyzing the space Iayout and the configuration of octree,this paper presents an improved octree for 3D representation.From the experimental results for 3D reconstruction of medical images,we can see the proposed method is superior to the traditional method in terms of the storing structure and visiting way,etc.

scholarly journals IBK – A NEW TOOL FOR MEDICAL IMAGE PROCESSING

2010 ◽  
Vol 13 (4) ◽  
pp. 20-27
Author(s):  
Linh Duy Tran ◽  
Linh Quang Huynh
Keyword(s):  
Image Processing ◽  
Diagnostic Imaging ◽  
3D Reconstruction ◽  
Programming Language ◽  
Medical Image ◽  
Medical Images ◽  
Rapid Development ◽  
Medical Image Processing ◽  
Matlab Programming

Along with the rapid development of diagnostic imaging equipment, software for medical image processing has played an important role in helping doctors and clinicians to reach accurate diagnoses. In this paper, methods to build a multipurpose tool based on Matlab programming language and its applications are presented. This new tool features enhancement, segmentation, registration and 3D reconstruction for medical images obtained from commonly used diagnostic imaging equipment.

scholarly journals Effects of Different Parameter Settings for 3D Data Smoothing and Mesh Simplification on Towards Near Real-Time 3D Reconstruction of High Resolution Bioceramic Bone Void Filling Medical Images

2021 ◽  
Author(s):  
Daniel Jie Yuan Chin ◽  
Ahmad Sufril Azlan Mohamed ◽  
Khairul Anuar Shariff ◽  
Mohd Nadhir Ab Wahab ◽  
Kunio Ishikawa
Keyword(s):  
Real Time ◽  
Volume Rendering ◽  
Medical Images ◽  
Three Dimensional ◽  
Mesh Simplification ◽  
Surface Rendering ◽  
Reconstruction Accuracy ◽  
Total Execution Time ◽  
Data Smoothing ◽  
Dimensional Reconstruction

Three-dimensional reconstruction plays an important role in assisting doctors and surgeons in diagnosing bone defects’ healing progress. Common three-dimensional reconstruction methods include surface and volume rendering. As the focus is on the shape of the bone, volume rendering is omitted. Many improvements have been made on surface rendering methods like Marching Cubes and Marching Tetrahedra, but not many on working towards real-time or near real-time surface rendering for large medical images, and studying the effects of different parameter settings for the improvements. Hence, in this study, an attempt towards near real-time surface rendering for large medical images is made. Different parameter values are experimented on to study their effect on reconstruction accuracy, reconstruction and rendering time, and the number of vertices and faces. The proposed improvement involving three-dimensional data smoothing with convolution kernel Gaussian size 0.5 and mesh simplification reduction factor of 0.1, is the best parameter value combination for achieving a good balance between high reconstruction accuracy, low total execution time, and a low number of vertices and faces. It has successfully increased the reconstruction accuracy by 0.0235%, decreased the total execution time by 69.81%, and decreased the number of vertices and faces by 86.57% and 86.61% respectively.

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