An optimization algorithm based on Bi-SIFT for SAR image registration

2015 ◽  
Author(s):  
Fuqiang Liu Fuqiang Liu ◽  
Fukun Bi Fukun Bi ◽  
Liang Chen Liang Chen ◽  
Hang Wei Hang Wei ◽  
Wenjie Tie Wenjie Tie
Keyword(s):  
Image Registration ◽  
Optimization Algorithm ◽  
Sar Image

Multi-view SAR image registration based on feature extraction

2021 ◽  
Author(s):  
JiaZheng Sun ◽  
Hui Wang ◽  
ShiChao Zheng ◽  
ZhaoYang Zeng ◽  
Xiang Chen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Image Registration ◽  
Sar Image

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.

scholarly journals Feature-Area Optimization: A Novel SAR Image Registration Method

2016 ◽  
Vol 13 (2) ◽  
pp. 242-246 ◽  
Author(s):  
Fuqiang Liu ◽  
Fukun Bi ◽  
Liang Chen ◽  
Hao Shi ◽  
Wei Liu
Keyword(s):  
Image Registration ◽  
Sar Image ◽  
Registration Method ◽  
Area Optimization

scholarly journals Analysis and Research of Subclinical Diabetic Peripheral Vascular Lesions Based on Mutual Information-based Medical Image Registration Algorithm and Colour Ultrasound Imaging (Preprint)

2020 ◽  
Author(s):  
Nailong Jia ◽  
Long Fan ◽  
Chuanzi Li ◽  
Zhongshi Nie ◽  
Suihuang Wang ◽  
...  
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Blood Vessels ◽  
Optimization Algorithm ◽  
Peripheral Blood ◽  
Medical Image ◽  
Hybrid Optimization ◽  
Medical Image Registration ◽  
Hybrid Optimization Algorithm ◽  
Subclinical Diabetes

BACKGROUND Background: At present, the incidence of diabetes is on the rise. When doctors diagnose and treat patients' diseases, they often need to image patients to provide complementary information on patient anatomy and functional metabolism. OBJECTIVE Objective: The aim was to understand the morphological features of peripheral blood vessels of diabetes more accurately and explore its Risk factors for the occurrence of lesions for early diagnosis and early prevention. METHODS Methods: The paper selected subclinical diabetes patients admitted to our hospital from October 2013 to October 2018 as a research object. After performing colour Doppler ultrasonography on peripheral blood vessels, images of ultrasound images were taken. Then the paper proposes a multi-mode medical image registration method based on hybrid optimization algorithm for the multi-extreme problem of mutual information function. Mutual information is used as the similarity measure. The hybrid optimization algorithm is used to search for the best registration exchange parameters. The quasi-colour super images are exchanged for registration purposes. RESULTS Results: The experimental results show that the hybrid optimization algorithm can accurately analyse the colour ultrasound image of the peripheral blood vessels of subclinical diabetes, avoiding falling into the local optimal value, and the accuracy of the registration result reaches the sub-pixel level. CONCLUSIONS Conclusion: With the rapid development of imaging technology, the increasing image resolution, and the increasing amount of image data, parallel performance is high. The quasi-method has a very important significance for multi-modal medical image registration. The parameters in this algorithm can be further optimized. CLINICALTRIAL

Cluster Based Medical Image Registration Using Optimized Neural Network

2017 ◽  
pp. 1437-1467
Author(s):  
Joydev Hazra ◽  
Aditi Roy Chowdhury ◽  
Paramartha Dutta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Image Registration ◽  
Convergence Rate ◽  
Optimization Algorithm ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Learning Method ◽  
Local Minima ◽  
The Neural Network

Registration of medical images like CT-MR, MR-MR etc. are challenging area for researchers. This chapter introduces a new cluster based registration technique with help of the supervised optimized neural network. Features are extracted from different cluster of an image obtained from clustering algorithms. To overcome the drawback regarding convergence rate of neural network, an optimized neural network is proposed in this chapter. The weights are optimized to increase the convergence rate as well as to avoid stuck in local minima. Different clustering algorithms are explored to minimize the clustering error of an image and extract features from suitable one. The supervised learning method applied to train the neural network. During this training process an optimization algorithm named Genetic Algorithm (GA) is used to update the weights of a neural network. To demonstrate the effectiveness of the proposed method, investigation is carried out on MR T1, T2 data sets. The proposed method shows convincing results in comparison with other existing techniques.

Sign in / Sign up

Export Citation Format

Share Document