scholarly journals Multimodal Image Fusion of Magnetic Resonance and Computed Tomography Brain Images – A New Approach

2020 ◽  
Vol 13 (03) ◽  
pp. 1523-1532
Author(s):  
Leena Chandrashekar ◽  
Sreedevi A
Keyword(s):  
Computed Tomography ◽  
Magnetic Resonance ◽  
Image Fusion ◽  
Brain Images ◽  
New Approach ◽  
Multimodal Image Fusion

Magnetic resonance and computed tomography image fusion using saliency map and cross bilateral filter

2019 ◽  
Vol 13 (6) ◽  
pp. 1157-1164 ◽  
Author(s):  
M. Munawwar Iqbal Ch ◽  
M. Mohsin Riaz ◽  
Naima Iltaf ◽  
Abdul Ghafoor ◽  
Muhammad Attique Sadiq
Keyword(s):  
Computed Tomography ◽  
Magnetic Resonance ◽  
Image Fusion ◽  
Bilateral Filter ◽  
Saliency Map ◽  
Tomography Image ◽  
Cross Bilateral Filter ◽  
Computed Tomography Image

scholarly journals Improved Guided Image Fusion for Magnetic Resonance and Computed Tomography Imaging

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Amina Jameel ◽  
Abdul Ghafoor ◽  
Muhammad Mohsin Riaz
Keyword(s):  
Computed Tomography ◽  
Magnetic Resonance ◽  
Image Fusion ◽  
Minimum Mean Square Error ◽  
Error Estimator ◽  
Gaussian Filter ◽  
Computed Tomography Imaging ◽  
Tomography Imaging ◽  
Guided Filtering ◽  
Different Levels

Improved guided image fusion for magnetic resonance and computed tomography imaging is proposed. Existing guided filtering scheme uses Gaussian filter and two-level weight maps due to which the scheme has limited performance for images having noise. Different modifications in filter (based on linear minimum mean square error estimator) and weight maps (with different levels) are proposed to overcome these limitations. Simulation results based on visual and quantitative analysis show the significance of proposed scheme.

scholarly journals Improved Localization of Implanted Subdural Electrode Contacts on Magnetic Resonance Imaging With an Elastic Image Fusion Algorithm in an Invasive Electroencephalography Recording

2014 ◽  
Vol 10 (4) ◽  
pp. 506-513 ◽  
Author(s):  
Lennart Henning Stieglitz ◽  
Christian Ayer ◽  
Kaspar Schindler ◽  
Markus Florian Oertel ◽  
Roland Wiest ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Magnetic Resonance ◽  
Image Fusion ◽  
Magnetic Resonance Images ◽  
Midline Shift ◽  
Fusion Algorithm ◽  
Pharmacoresistant Epilepsy ◽  
Contact Shift ◽  
Subdural Electrode ◽  
Standard Linear

Abstract BACKGROUND: Accurate projection of implanted subdural electrode contacts in presurgical evaluation of pharmacoresistant epilepsy cases by invasive electroencephalography is highly relevant. Linear fusion of computed tomography and magnetic resonance images may display the contacts in the wrong position as a result of brain shift effects. OBJECTIVE: A retrospective study in 5 patients with pharmacoresistant epilepsy was performed to evaluate whether an elastic image fusion algorithm can provide a more accurate projection of the electrode contacts on the preimplantation magnetic resonance images compared with linear fusion. METHODS: An automated elastic image fusion algorithm (AEF), a guided elastic image fusion algorithm (GEF), and a standard linear fusion algorithm were used on preoperative magnetic resonance images and postimplantation computed tomography scans. Vertical correction of virtual contact positions, total virtual contact shift, corrections of midline shift, and brain shifts caused by pneumocephalus were measured. RESULTS: Both AEF and GEF worked well with all 5 cases. An average midline shift of 1.7 mm (SD, 1.25 mm) was corrected to 0.4 mm (SD, 0.8 mm) after AEF and to 0.0 mm (SD, 0 mm) after GEF. Median virtual distances between contacts and cortical surface were corrected by a significant amount, from 2.3 mm after linear fusion algorithm to 0.0 mm after AEF and GEF (P < .001). Mean total relative corrections of 3.1 mm (SD, 1.85 mm) after AEF and 3.0 mm (SD, 1.77 mm) after GEF were achieved. The tested version of GEF did not achieve a satisfying virtual correction of pneumocephalus. CONCLUSION: The technique provided a clear improvement in fusion of preimplantation and postimplantation scans, although the accuracy is difficult to evaluate.

scholarly journals Multimodal non-rigid registration methods based on local variability measures in computed tomography and magnetic resonance brain images

2014 ◽  
Vol 8 (12) ◽  
pp. 699-707 ◽  
Author(s):  
Isnardo Reducindo ◽  
Elisa Scalco ◽  
Edgar R. Arce-Santana ◽  
Giovanni M. Cattaneo ◽  
Aldo R. Mejia-Rodriguez ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Magnetic Resonance ◽  
Brain Images ◽  
Rigid Registration ◽  
Local Variability
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