Intensity Gradient Based Registration and Fusion of Multi-modal Images

2007 ◽  
Vol 46 (03) ◽  
pp. 292-299 ◽  
Author(s):  
J. Modersitzki ◽  
E. Haber
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Numerical Optimization ◽  
Distance Measure ◽  
State Of The Art ◽  
Intensity Gradient ◽  
Alternative Approach ◽  
Main Disadvantage ◽  
Gradient Based ◽  
Multimodal Images

Summary Objectives: A particular problem in image registration arises for multi-modal images taken from different imaging devices and/or modalities. Starting in 1995, mutual information has shown to be a very successful distance measure for multi-modal image registration. Therefore, mutual information is considered to be the state-of-the-art approach to multi-modal image registration. However, mutual information has also a number of well-known drawbacks. Its main disadvantage is that it is known to be highly non-convexand hastypicallymanylocal maxima. Methods: This observation motivates us to seek a different image similarity measure which is better suited for optimization but as well capable to handle multimodal images. Results: In this work, we investigate an alternative distance measure which is based on normalized gradients. Conclusions: As we show, the alternative approach is deterministic, much simpler, easier to interpret, fast and straightforward to implement, faster to compute, and also much more suitable to numerical optimization.

scholarly journals Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 99 ◽  
Author(s):  
Kleopatra Pirpinia ◽  
Peter A. N. Bosman ◽  
Jan-Jakob Sonke ◽  
Marcel van Herk ◽  
Tanja Alderliesten
Keyword(s):  
Machine Learning ◽  
Image Registration ◽  
State Of The Art ◽  
Deformable Image Registration ◽  
Optimization Approach ◽  
High Quality ◽  
Trade Off ◽  
Multi Objective ◽  
Current State ◽  
Image Artefacts

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

A Novel Coarse-to-Fine Scheme for Automatic Image Registration Based on SIFT and Mutual Information

2014 ◽  
Vol 52 (7) ◽  
pp. 4328-4338 ◽  
Author(s):  
Maoguo Gong ◽  
Shengmeng Zhao ◽  
Licheng Jiao ◽  
Dayong Tian ◽  
Shuang Wang
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Coarse To Fine

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.

Self-similarity weighted mutual information: A new nonrigid image registration metric

2014 ◽  
Vol 18 (2) ◽  
pp. 343-358 ◽  
Author(s):  
Hassan Rivaz ◽  
Zahra Karimaghaloo ◽  
D. Louis Collins
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Self Similarity ◽  
Nonrigid Image Registration

scholarly journals Automated Dental Identification with Lowest Cost Path-Based Teeth and Jaw Separation

2016 ◽  
Vol 22 (2) ◽  
pp. 44-56 ◽  
Author(s):  
Jan-Vidar Ølberg ◽  
Morten Goodwin
Keyword(s):  
High Stability ◽  
Distance Measure ◽  
State Of The Art ◽  
Test Set ◽  
X Ray ◽  
Dental Identification ◽  
Verification Process ◽  
Stability And Accuracy ◽  
New System ◽  
Dental Work

Abstract Teeth are some of the most resilient tissues of the human body. Because of their placement, teeth often yield intact indicators even when other metrics, such as finger prints and DNA, are missing. Forensics on dental identification is now mostly manual work which is time and resource intensive. Systems for automated human identification from dental X-ray images have the potential to greatly reduce the necessary efforts spent on dental identification, but it requires a system with high stability and accuracy so that the results can be trusted. This paper proposes a new system for automated dental X-ray identification. The scheme extracts tooth and dental work contours from the X-ray images and uses the Hausdorff-distance measure for ranking persons. This combination of state-of-the-art approaches with a novel lowest cost path-based method for separating a dental X-ray image into individual teeth, is able to achieve comparable and better results than what is available in the literature. The proposed scheme is fully functional and is used to accurately identify people within a real dental database. The system is able to perfectly separate 88.7% of the teeth in the test set. Further, in the verification process, the system ranks the correct person in top in 86% of the cases, and among the top five in an astonishing 94% of the cases. The approach has compelling potential to significantly reduce the time spent on dental identification.

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

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