Region of interest three-dimensional visualization in clinical human CT images sequence

Medical image analysis is more than just algorithms. Visualization of the original image data and processed results, interaction with the data, as well as the data themselves are also important. It has been widely used for demonstrating lesions or their localization in the musculoskeletal system, vascular system, respiratory and alimentary systems. Region of interest (ROI) imaging visualization techniques in computed tomography (CT), which can visualize an ROI image from the CT sequence data set of the ROI, can be used not only for reducing imaging-time but also for potentially increasing clinical analysis accuracy of the particular area images. VC++6.0 with visualization toolkit (VTK) are adopted to reconstruct the 3D images using the 2D CT image sequence in DICOM format. The ROI images visualization by use of the marching cubes (MC) algorithm from the clinical human CT data. The experimental results show that this method is robust, easy to implement and excellent image quality evaluation. Because the proposed method requires minimum visualization data for clinical analysis, it can reduce imaging-time, and has excellent image quality evaluation. These methods can assist doctors to make better and more accurate diagnosis.

[1]  Vikash Ravi Goel,et al.  Mathematical analysis of DICOM CT datasets: can endograft sizing be automated for complex anatomy? , 2008, Journal of vascular surgery.

[2]  Brent Liu,et al.  Integrating DICOM structure reporting (SR) into the medical imaging informatics data grid , 2008, SPIE Medical Imaging.

[3]  N. K. Bose Miniaturised computational imaging system design for super-resolution, large field-of-view and extended depth-of-field with applications to surveillance, medical imaging, and condition-based maintenance , 2008 .

[4]  F. Salaffi,et al.  The crowned dens syndrome as a cause of neck pain: clinical and computed tomography study in patients with calcium pyrophosphate dihydrate deposition disease. , 2008, Clinical and experimental rheumatology.

[5]  Ken Martin,et al.  Corrections to "Time Dependent Processing in a Parallel Pipeline Architecture' , 2008, IEEE Trans. Vis. Comput. Graph..

[6]  Robert van Liere,et al.  A multimodal virtual reality interface for 3 D interaction with VTK , 2007 .

[7]  M. Grgic,et al.  Efficient presentation of DICOM mammography images using Matlab , 2008, 2008 15th International Conference on Systems, Signals and Image Processing.

[8]  C. Ahmad,et al.  Simulation of surgical glenoid resurfacing using three-dimensional computed tomography of the arthritic glenohumeral joint: the amount of glenoid retroversion that can be corrected. , 2009, Journal of shoulder and elbow surgery.

[9]  M Mupparapu,et al.  Cone beam computed tomography findings in a case of plexiform ameloblastoma. , 2009, Quintessence international.

[10]  W E Bolch,et al.  Marching cube algorithm: review and trilinear interpolation adaptation for image-based dosimetric models. , 2003, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[11]  R Turchetta,et al.  Empirical electro-optical and x-ray performance evaluation of CMOS active pixels sensor for low dose, high resolution x-ray medical imaging. , 2007, Medical physics.

[12]  Sundaresan Raman,et al.  Quality Isosurface Mesh Generation Using an Extended Marching Cubes Lookup Table , 2008, Comput. Graph. Forum.

[13]  P. Papagelopoulos,et al.  A three-dimensional medical imaging model for quantitative assessment of proximal tibia vs. anterior iliac crest cancellous bone. , 2008, The Knee.

[14]  Jie Liu,et al.  Simplified patterns for extracting the isosurfaces of solid objects , 2008, Image Vis. Comput..

[15]  William Schroeder,et al.  The Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics , 1997 .

[16]  Boqiang Liu,et al.  Medical Image Conversion with DICOM , 2007, 2007 Canadian Conference on Electrical and Computer Engineering.

[17]  K S Nikita,et al.  A novel and efficient implementation of the marching cubes algorithm. , 2001, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[18]  John C. J. Chiou Virtual Clay: An Enhanced Marching Cubes Algorithm for In-Process Geometry Modeling , 2007 .

[19]  B. Nicolas Bloch,et al.  The role of magnetic resonance imaging (MRI) in prostate cancer imaging and staging at 1.5 and 3 Tesla: the Beth Israel Deaconess Medical Center (BIDMC) approach. , 2008, Cancer biomarkers : section A of Disease markers.

[20]  Mathias Fink,et al.  Green's function estimation in speckle using the decomposition of the time reversal operator: application to aberration correction in medical imaging. , 2008, The Journal of the Acoustical Society of America.

[21]  Ken Martin,et al.  Time Dependent Processing in a Parallel Pipeline Architecture , 2007, IEEE Transactions on Visualization and Computer Graphics.