scholarly journals GeoInterp: Contour Interpolation with Geodesic Snakes Geodesic Snakes

2006 ◽  
Author(s):  
Rohit Saboo ◽  
Rosenman Julian ◽  
Pizer Stephen
Keyword(s):  
Medical Images ◽  
Three Dimensional ◽  
Contour Interpolation

The process of outlining organs on three dimensional medical images is extremely time-consuming. In this document, we describe a new tool that can interpolate contours and also make them smooth. The tool uses geodesic snakes to perform these functions. The implementation uses The Insight Toolkit and the implementation of geodesic snakes that comes with SNAP.

Automatic recognition of anatomical regions in three-dimensional medical images

2016 ◽  
Vol 76 ◽  
pp. 120-133
Author(s):  
Márton Tóth ◽  
László Ruskó ◽  
Balázs Csébfalvi
Keyword(s):  
Medical Images ◽  
Three Dimensional ◽  
Automatic Recognition

scholarly journals Three-dimensional contour interpolation: Testing the 90-degree constraint

2010 ◽  
Vol 2 (7) ◽  
pp. 356-356
Author(s):  
P. Garrigan ◽  
P. J. Kellman
Keyword(s):  
Three Dimensional ◽  
Contour Interpolation ◽  
Degree Constraint

scholarly journals 3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images

2021 ◽  
pp. 24-34
Author(s):  
Sungmin Hong ◽  
Razvan Marinescu ◽  
Adrian V. Dalca ◽  
Anna K. Bonkhoff ◽  
Martin Bretzner ◽  
...  
Keyword(s):  
Medical Images ◽  
Three Dimensional ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Generative Modeling

Evaluation of Spherical Parameterization Methods for Three-Dimensional Reconstruction of Medical Images

2013 ◽  
Vol 365-366 ◽  
pp. 1342-1349
Author(s):  
Xing Hui Wu ◽  
Zhi Xiu Hao
Keyword(s):  
Medical Images ◽  
Three Dimensional ◽  
3D Models ◽  
Mapping Method ◽  
Geometric Error ◽  
Patient Specific ◽  
Shape Modelling ◽  
Dimensional Reconstruction ◽  
Statistical Shape ◽  
Spherical Parameterization

The spherical parameterization is important for the correspondence problem that is a major part of statistical shape modelling for the reconstruction of patient-specific 3D models from medical images. In this paper, we present comparative studies of five common spherical mapping methods applied to the femur and tibia models: the Issenburg et al. method, the Alexa method, the Saba et al. method, the Praun et al. method and the Shen et al. method. These methods are evaluated using three sets of measures: distortion property, geometric error and distance to standard landmarks. Results show that the Praun et al. method performs better than other methods while the Shen et al. method can be regarded as the most reliable one for providing an acceptable correspondence result. We suggest that the area preserving property can be used as a sufficient condition while the angle preserving property is not important when choosing a spherical mapping method for correspondence application.

Open Source Applications for Image Visualization and Processing in Neuroimaging Training

2015 ◽  
pp. 1319-1332
Author(s):  
Juan A. Juanes ◽  
Pablo Ruisoto ◽  
Alberto Prats-Galino ◽  
Andrés Framiñán
Keyword(s):  
Magnetic Resonance ◽  
Medical Imaging ◽  
Open Source ◽  
Medical Images ◽  
Three Dimensional ◽  
Mr Images ◽  
Image Visualization ◽  
3D Slicer ◽  
Volumetric Reconstruction ◽  
Key Features

The aim of this paper is to demonstrate the major role and potential of three of the most powerful open source computerized tools for the advanced processing of medical images, in the study of neuroanatomy. DICOM images were acquired with radiodiagnostic equipment using 1.5 Tesla Magnetic Resonance (MR) images. Images were further processed using the following applications: first, OsiriXTM version 4.0 32 bits for OS; Second, 3D Slicer version 4.3; and finally, MRIcron, version 6. Advanced neuroimaging processing requires two key features: segmentation and three-dimensional or volumetric reconstruction. Examples of identification and reconstruction of some of the most complex neuroimaging elements such vascular ones and tractographies are included in this paper. The three selected applications represent some of the most versatile technologies within the field of medical imaging.

Medical images segmentation based on improved three-dimensional pulse coupled neural network

2017 ◽  
Vol 13 (1) ◽  
pp. 72 ◽  
Author(s):  
Maxin Wang ◽  
Xinzheng Xu ◽  
Guanying Wang ◽  
Shifei Ding ◽  
Tongfeng Sun
Keyword(s):  
Neural Network ◽  
Medical Images ◽  
Three Dimensional ◽  
Pulse Coupled Neural Network

Visual search in three-dimensional non-medical images: visual-motor performance of radiologists

2019 ◽  
Author(s):  
Tatjana Pladere ◽  
Mara Velina ◽  
Viktorija Andriksone ◽  
Reinis Pitura ◽  
Karola Panke ◽  
...  
Keyword(s):  
Visual Search ◽  
Motor Performance ◽  
Medical Images ◽  
Three Dimensional ◽  
Visual Motor
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