scholarly journals Complex Dual Tree Wavelet Multi-Level Feature Based Transformation Parameter Estimation for 3d Medical Image Registration

2019 ◽  
Vol 8 (9) ◽  
pp. 799-806
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
3D Images ◽  
Low Pass ◽  
Feature Based ◽  
Information Metric ◽  
Transformation Parameters ◽  
3D Image Registration ◽  
High Pass ◽  
Ct And Mri

3D image registration of CT and MRI data is carried out using DTCWT sub bands by considering the features from all 64 bands. The features are selected by considering Mattes Mutual Information Metric and the optimizer algorithm estimates the optimum transformation parameters from all the 64 bands. Transformation parameters from eight low pass bands from each octave are averaged to identify optimum registration parameters. Similarly, for registration of high pass bands mean of transformation parameters from 56 bands are identified. The proposed registration algorithm is suitable for register multimodal medical images and the proposed algorithm is validated for more than 20 3D images. Mutual information and joint entropy is estimated to demonstrate the advantages of proposed algorithm overt that of intensity based algorithm. With features identified from 56 bands with six orientations the registered image is found to consist of features from both input images with closeness level measured to be within 12%.

Computation of Mutual Information Metric for Image Registration on Multiple GPUs

2014 ◽  
pp. 208-217
Author(s):  
Andrew Adinetz ◽  
Jiri Kraus ◽  
Markus Axer ◽  
Marcel Huysegoms ◽  
Stefan Köhnen ◽  
...  
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Multiple Gpus ◽  
Information Metric

An Improved Demon Registration with Mutual Information for Non-Rigid Medical Images

2013 ◽  
Vol 284-287 ◽  
pp. 1622-1626 ◽  
Author(s):  
Xiao Hui Xie ◽  
Cui Ma ◽  
Qiang Sun ◽  
Ru Xu Du
Keyword(s):  
Image Analysis ◽  
Image Registration ◽  
Mutual Information ◽  
Critical Points ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Good Method ◽  
Demons Algorithm ◽  
Single Modality ◽  
Ct And Mri

Non-rigid image registration plays an important role in medical imaging. Classic Demons algorithm is a good method for image registration in some domain. One disadvantage of classic Demons algorithm is that the topological preservation can not be ensured, and it can only adapt to deal with the single modality image registration. In medical image analysis, the different modal images comparison and fusion are needed to give the doctor enough information for making a decision. The mutual information algorithm has been validated useful for multi-modality image registration. By analyzing the critical points of Demons registration like mis-registration, an improved Demons algorithm with mutual information evaluation is proposed. Experiment results on liver images between CT and MRI modality show that the proposed algorithm can deal with multi-modality image registration well and it can hold the abilities even faces the noise and distortion.

scholarly journals Incorporating Metric Flows and Sparse Jacobian Transformations in ITK

2006 ◽  
Author(s):  
Mathieu De craene ◽  
Aloys Du bois d'aische ◽  
Benoit Macq ◽  
Simon Warfield
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Computation Time ◽  
Deformable Registration ◽  
Deformation Field ◽  
Transformation Model ◽  
Rigid Registration ◽  
Information Metric ◽  
Demons Algorithm

Various metrics have been proposed in the literature for performing intrinsic automatic image to image registration. Among these measures, mutual information is a very popular one because of its robustness and accuracy for a wide variety of applications. In this paper, we propose a filter for performing non-rigid registration by estimating a dense deformation field derived from the mutual information metric. This filter takes place in the ITK PDE deformable registration design like the Demons algorithm of Thirion. We also show how the concept of metric flow is conceptually linked to the concept of metric derivative for a prior transformation model by the transformation jacobian. We also suggest a sparse implementation of the GetJacobian() method for reducing the computation time of a metric derivative for local transformations models.

scholarly journals Non-rigid image registration using a modified fuzzy feature-based inference system for 3D cardiac motion estimation

2021 ◽  
Vol 205 ◽  
pp. 106085
Author(s):  
Monire Sheikh Hosseini ◽  
Mahammad Hassan Moradi ◽  
Mahdi Tabassian ◽  
Jan D'hooge
Keyword(s):  
Image Registration ◽  
Motion Estimation ◽  
Cardiac Motion ◽  
Inference System ◽  
Feature Based

CURRENT-MODE ACTIVE-C FILTER EMPLOYING REDUCED NUMBER OF CCCII+s

2007 ◽  
Vol 16 (04) ◽  
pp. 507-516 ◽  
Author(s):  
SHAHRAM MINAEI ◽  
ERKAN YUCE
Keyword(s):  
Current Mode ◽  
Pass Band ◽  
Passive Component ◽  
Resistorless Filter ◽  
Low Pass ◽  
Component Matching ◽  
Band Pass ◽  
High Output Impedance ◽  
Quality Factor Q ◽  
High Pass

In this paper, a universal current-mode second-order active-C filter for simultaneously realizing low-pass, band-pass and high-pass responses is proposed. The presented filter employs only three plus-type second-generation current-controlled conveyors (CCCII+s). This filter needs no critical active and passive component matching conditions and no additional active and passive elements for realizing high output impedance low-pass, band-pass and high-pass characteristics. The angular resonance frequency (ω0) and quality factor (Q) of the proposed resistorless filter can be tuned electronically. To verify the theoretical analysis and to exhibit the performance of the proposed filter, it is simulated with SPICE program.

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