Ant colony optimization image registration algorithm based on wavelet transform and mutual information

2013 ◽  
Author(s):  
Hong Zhang ◽  
Yanfeng Sun ◽  
Bing Zhai ◽  
Yiding Wang
Keyword(s):  
Wavelet Transform ◽  
Image Registration ◽  
Mutual Information ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Registration Algorithm ◽  
Image Registration Algorithm

SAR Image Registration Using Ratio Mutual Information

2012 ◽  
Vol 241-244 ◽  
pp. 2630-2637
Author(s):  
Chun Rong Wei ◽  
Chu He ◽  
Hong Sun
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Speckle Noise ◽  
Synthetic Aperture Radar Image ◽  
Reference Image ◽  
Sar Image ◽  
Local Maxima ◽  
Characteristic Quantity ◽  
Registration Algorithm ◽  
Image Registration Algorithm

In order to reduce the noise sensitivity of the SAR (synthetic aperture radar) image registration, a image registration algorithm which basing on the ratio mutual information (RatioMI) is proposed in this paper. Firstly, the ratio images of the reference image and the floating image are gotten by using the ratio operator, and then take the two ratio images as a similar characteristic quantity to construct the similarity measure function which was used in the optimization process of the image registration experiment. The experimental results of the SAR image registration show that the new registration algorithm which based on the RatioMI is effectively in avoiding the local maxima point problems causing by speckle noise.

Effect of Measure and Interpolation Methods for Intensity-Based Medical Image Registration

2013 ◽  
Vol 647 ◽  
pp. 612-617
Author(s):  
Guo Dong Zhang ◽  
Xiao Hu Xue ◽  
Wei Guo
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Medical Image ◽  
Medical Image Registration ◽  
Registration Accuracy ◽  
Partial Volume ◽  
Interpolation Methods ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Local Extreme

The local extreme is main reason to hamper the optimization process and influence the registration accuracy in medical image registration algorithm. In general, the accuracy of image registration based on mutual information is afforded by interpolation methods. In this paper, we analyze the effect of the measure and interpolation methods for medical image registration and present a medical image registration algorithm using mutual strictly concave function measure and partial volume (PV) interpolation methods. The experiment results show that for images with low local correlation the algorithm has the ability to reduce the local extreme, the registration accuracy is improved, and the algorithm expended less time than mutual information based registration method with partial volume (PV) or generalized partial volume estimation (GPVE).

scholarly journals Evaluation Method for the Optimization of 3D Rigid Image Registration on Multimodal Image Datasets

2019 ◽  
Vol 9 (1) ◽  
pp. 5539-5545
Keyword(s):  
Computed Tomography ◽  
Image Registration ◽  
Mutual Information ◽  
Planning System ◽  
Treatment Planning System ◽  
Mean Square ◽  
Similarity Metric ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Image Datasets

Optimization based three dimensional (3D) rigid image registration (RIR) is one of the most commonly used methods of image registration in radiotherapy. Interpolator and similarity metric plays a crucial role in optimization image registration process. In this paper, the efficiency of image registration algorithm is analyzed by using various combinations of interpolators and similarity metric in terms of quantitative measures and is compared with commercially available image registration algorithm in radiotherapy. Computed Tomography (CT) and Cone Beam Computed Tomography (CBCT) image datasets were registered by image registration algorithm written in python language using simple image tool kit (SITK). Different combinations of similarity metric and interpolator such as mean square difference (MSD), mutual information (MI), demons and nearest neighbor (NN), linear, B- spline respectively were used in this study. The efficiency of the algorithm was quantified in terms of mean square error (MSE), structural similarity index (SSI), normalized cross correlation (NCC) and mutual information (MI). The image registration algorithm with most efficient combination of similarity metric and interpolator was selected for comparison with the commercially available image registration algorithm. The algorithm for multimodal (CTCBCT) 3D image registration with NN interpolator and MI similarity metric showed the highest values of SSI, NCC and MI as 0.865, 0.933, 1.223 respectively among other combination of interpolator and similarity metric. Further this algorithm when compared and statistically analyzed with commercially available image registration algorithm of Treatment Planning System (TPS. most commonly used for radiotherapy treatment) resulted in no significant difference (F value NCC-3.18, MI-4.010, SSI2.776) in their quantitative measures. The present study is limited to 3D RIR and can be extended for deformable image registration.

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