Medical image alignment based on landmark- and approximate contour-matching

2021 ◽  
Vol 8 (06) ◽  
Author(s):  
Mia Mojica ◽  
Mihaela Pop ◽  
Mehran Ebrahimi
Keyword(s):  
Medical Image ◽  
Image Alignment ◽  
Contour Matching

scholarly journals Revisiting overlap invariance in medical image alignment

2008 ◽  
Author(s):  
Nathan D. Cahill ◽  
Julia A. Schnabel ◽  
J. Alison Noble ◽  
David J. Hawkes
Keyword(s):  
Medical Image ◽  
Image Alignment

EXAMINATION OF MEDICAL IMAGE RECORDING ACCORDING TO THE CATEGORY OF RESEARCH PROGRESS AND TECHNICAL METHODS BASED ON DEEP LEARNING

2021 ◽  
Vol 03 (01) ◽  
pp. 22-31
Author(s):  
Elcin Nizami Huseyn ◽  
◽  
Mohammad Hoseini ◽  
Keyword(s):  
Deep Learning ◽  
Image Matching ◽  
Medical Image ◽  
Image Alignment ◽  
Research Progress ◽  
Optimization Strategy ◽  
Convolutional Network ◽  
Image Recording ◽  
Guided Surgery ◽  
Similarity Estimation

With the development of imaging-guided surgery and radiotherapy, the clinical demand for medical image matching research is stronger and the challenges are even greater. In recent years, deep learning, especially deep convolution neural networks, has made excellent achievements in medical image processing, and the research on medical image matching has developed rapidly. In this paper, the domestic and foreign research progress based on deep learning medical image alignment is summarized according to the classification of technical methods, including the similarity estimation based on optimization strategy, the transformation parameters of direct estimation of medical image alignment, etc. Then it analyses the challenge of the deep learning method in medical image matching and puts forward possible solutions and research directions. Key words: medical image recording, deep learning, CNN, full convolutional network

PERFORMANCE COMPARISON OF THE POWELL AND PSO METHODS FOR MEDICAL IMAGE ALIGNMENT

2017 ◽  
Vol 29 (02) ◽  
pp. 1750014
Author(s):  
Meisen Pan ◽  
Fen Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Medical Image ◽  
Optimization Methods ◽  
Optimization Method ◽  
Performance Comparison ◽  
Image Alignment ◽  
Swarm Optimization ◽  
Similarity Metric ◽  
Transformation Parameters ◽  
Powell Method

In the course of aligning medical images, the similarity metric (also called alignment function) is regarded as the objective function, and the optimization method as the tool for exploring the optimal transformation parameters. In this paper, the medical image alignment is represented first and then the optimization methods are depicted in detail. With this description, by the use of the mutual information (MI) as the similarity metric, the Powell method and the particle swarm optimization (PSO) method are employed to seek the optimal transformation parameters respectively, and their optimization performances are estimated and compared. The experimental results show that the Powell and PSO methods can cater to both the multi-modality medical image alignments.

scholarly journals Unsupervised Medical Image Alignment With Curriculum Learning

2021 ◽  
Author(s):  
Mihail Burduja ◽  
Radu Tudor Ionescu
Keyword(s):  
Medical Image ◽  
Image Alignment

An overlap invariant entropy measure of 3D medical image alignment

1999 ◽  
Vol 32 (1) ◽  
pp. 71-86 ◽  
Author(s):  
C. Studholme ◽  
D.L.G. Hill ◽  
D.J. Hawkes
Keyword(s):  
Medical Image ◽  
Image Alignment ◽  
Entropy Measure

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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