Image fusion for multimodality image via domain transfer and nonrigid transformation

2020 ◽  
Author(s):  
Yicheng Yang ◽  
Huabing Zhou
Keyword(s):  
Image Fusion ◽  
Multimodality Image ◽  
Domain Transfer

Three Dimensional Imaging and Surgical Planning

1995 ◽  
Vol 112 (5) ◽  
pp. P169-P169
Author(s):  
Vincent N. Carrasco ◽  
Suresh K. Mukherji ◽  
Harold C. Pillsbury
Keyword(s):  
Mr Imaging ◽  
Image Fusion ◽  
Surgical Planning ◽  
Preoperative Planning ◽  
Three Dimensional ◽  
Educational Objectives ◽  
Three Dimensional Imaging ◽  
Multimodality Image ◽  
Multimodality Image Fusion

Educational objectives: To discuss the value of CT and MR imaging for preoperative surgical planning and to discuss computerized three-dimensional imaging, multimodality image fusion, and appreciate their role in surgical preoperative planning.

Improving Multimodality Image Fusion through Integrate AFL and Wavelet Transform

2017 ◽  
pp. 143-157
Author(s):  
Girraj Prasad Rathor ◽  
Sanjeev Kumar Gupta
Keyword(s):  
Fuzzy Logic ◽  
Wavelet Transform ◽  
Image Fusion ◽  
Signal To Noise Ratio ◽  
Fusion Rule ◽  
Weighted Averaging ◽  
Adaptive Fuzzy ◽  
Fused Image ◽  
Adaptive Fuzzy Logic ◽  
Multimodality Image

Image fusion based on different wavelet transform is the most commonly used image fusion method, which fuses the source pictures data in wavelet space as per some fusion rules. But, because of the uncertainties of the source images contributions to the fused image, to design a good fusion rule to incorporate however much data as could reasonably be expected into the fused picture turns into the most vital issue. On the other hand, adaptive fuzzy logic is the ideal approach to determine uncertain issues, yet it has not been utilized as a part of the outline of fusion rule. A new fusion technique based on wavelet transform and adaptive fuzzy logic is introduced in this chapter. After doing wavelet transform to source images, it computes the weight of each source images coefficients through adaptive fuzzy logic and then fuses the coefficients through weighted averaging with the processed weights to acquire a combined picture: Mutual Information, Peak Signal to Noise Ratio, and Mean Square Error as criterion.

Multimodality image fusion using centre-based genetic algorithm and fuzzy logic

2018 ◽  
Vol 28 (4) ◽  
pp. 322 ◽  
Author(s):  
Subbiah Parvathy Velmurugan ◽  
Pothiraj Sivakumar ◽  
Murugan Pallikonda Rajasekaran
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Image Fusion ◽  
Multimodality Image ◽  
Multimodality Image Fusion

scholarly journals Validation of Automated Biventricular Myocardial Segmentation from Coronary Computed Tomographic Angiography for Multimodality Image Fusion

2021 ◽  
Author(s):  
Marina Piccinelli ◽  
Navdeep Dahiya ◽  
Russell D Folks ◽  
Anthony Yezzi ◽  
Ernest V Garcia
Keyword(s):  
Image Fusion ◽  
Correlation Coefficients ◽  
Computed Tomographic Angiography ◽  
Dice Similarity Coefficient ◽  
Clinical Environment ◽  
Myocardial Mass ◽  
Computed Tomographic ◽  
Coronary Ct ◽  
Cardiac Segmentation ◽  
Multimodality Image

AbstractPurposeImage fusion strategies of myocardial perfusion imaging (MPI) and coronary CT angiography (CCTA) have shown increased diagnostic power. However, their clinical feasibility is hindered by the lack of efficient algorithms for the extraction of cardiac anatomy from CCTA datasets. The aim of this work was to validate our previously published algorithm for automated cardiac segmentation of CCTAs in a larger cohort of subjects while testing its application in clinical settings.MethodsThree borders were automatically and manually extracted on sixty-three clinical CCTAs: left and right endocardia (LV, RV) and the biventricular epicardium (EPI). Impact of image resolutions and inter-operator variability on accuracy and robustness of automated processing were evaluated. Automated algorithm accuracy was assessed with the Dice Similarity Coefficient (DSC) and the surface-to-surface distance metric. Relevant quantities were compared for automated versus manual segmentations: LV and RV volumes, myocardial mass and LV myocardial mass.ResultsLower resolution images offered an acceptable trade-off for accuracy and processing time (45 sec). DSC for LV, RV, EPI borders were 0.88, 0.80 and 0.89. Automated versus manual correlation coefficients for LV and RV vol, myo and LV mass were 0.96, 0.73, 0.84 and 0.67 with inter-operator agreement > 0.93 for three variables. Consistent and improved results were evidenced at higher resolutions.ConclusionOur algorithms allowed efficient automated cardiac segmentation from CT imagery with minimal user intervention, clinically acceptable times and accuracy. The reported results show promise for its use in a clinical environment, specifically in the context of image fusion.

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