Spatial Information in Entropy-Based Image Registration

2003 ◽  
pp. 132-141 ◽  
Author(s):  
Mert R. Sabuncu ◽  
Peter J. Ramadge
Keyword(s):  
Image Registration ◽  
Spatial Information

scholarly journals Nonrigid Medical Image Registration Using an Information Theoretic Measure Based on Arimoto Entropy with Gradient Distributions

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 189 ◽  
Author(s):  
Bicao Li ◽  
Huazhong Shu ◽  
Zhoufeng Liu ◽  
Zhuhong Shao ◽  
Chunlei Li ◽  
...  
Keyword(s):  
Image Registration ◽  
Cost Function ◽  
Medical Image ◽  
Spatial Information ◽  
Brain Magnetic Resonance Imaging ◽  
Dissimilarity Measure ◽  
Medical Image Registration ◽  
Three Dimension ◽  
Information Theoretic ◽  
Regularization Term

This paper introduces a new nonrigid registration approach for medical images applying an information theoretic measure based on Arimoto entropy with gradient distributions. A normalized dissimilarity measure based on Arimoto entropy is presented, which is employed to measure the independence between two images. In addition, a regularization term is integrated into the cost function to obtain the smooth elastic deformation. To take the spatial information between voxels into account, the distance of gradient distributions is constructed. The goal of nonrigid alignment is to find the optimal solution of a cost function including a dissimilarity measure, a regularization term, and a distance term between the gradient distributions of two images to be registered, which would achieve a minimum value when two misaligned images are perfectly registered using limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimization scheme. To evaluate the test results of our presented algorithm in non-rigid medical image registration, experiments on simulated three-dimension (3D) brain magnetic resonance imaging (MR) images, real 3D thoracic computed tomography (CT) volumes and 3D cardiac CT volumes were carried out on elastix package. Comparison studies including mutual information (MI) and the approach without considering spatial information were conducted. These results demonstrate a slight improvement in accuracy of non-rigid registration.

A CNN based Hybrid approach towards automatic image registration

2013 ◽  
Vol 62 (1) ◽  
pp. 33-49 ◽  
Author(s):  
Pattathal V. Arun ◽  
Sunil K. Katiyar
Keyword(s):  
Neural Network ◽  
Image Registration ◽  
Feature Matching ◽  
Spatial Information ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Cellular Neural Network ◽  
Contextual Knowledge ◽  
Neural Network Approach ◽  
Statistical Measures

Abstract Image registration is a key component of various image processing operations which involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however inability to properly model object shape as well as contextual information had limited the attainable accuracy. In this paper, we propose a framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as Vector Machines, Cellular Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN has found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using corset optimization The salient features of this work are cellular neural network approach based SIFT feature point optimisation, adaptive resampling and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. System has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

scholarly journals Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information

2019 ◽  
Vol 28 (7) ◽  
pp. 3584-3597 ◽  
Author(s):  
Johan Ofverstedt ◽  
Joakim Lindblad ◽  
Natasa Sladoje
Keyword(s):  
Image Registration ◽  
Spatial Information

scholarly journals Dimensionality reduction of medical image descriptors for multimodal image registration

2015 ◽  
Vol 1 (1) ◽  
pp. 201-205 ◽  
Author(s):  
Johanna Degen ◽  
Jan Modersitzki ◽  
Mattias P. Heinrich
Keyword(s):  
Image Registration ◽  
Dimensionality Reduction ◽  
Spatial Information ◽  
Contextual Information ◽  
Computation Time ◽  
Principal Component ◽  
Relevant Research ◽  
Multimodal Image Registration ◽  
Image Descriptors ◽  
Image Patches

AbstractDefining similarity forms a challenging and relevant research topic in multimodal image registration. The frequently used mutual information disregards contextual information, which is shared across modalities. A recent popular approach, called modality independent neigh-bourhood descriptor, is based on local self-similarities of image patches and is therefore able to capture spatial information. This image descriptor generates vectorial representations, i.e. it is multidimensional, which results in a disadvantage in terms of computation time. In this work, we present a problem-adapted solution for dimensionality reduction, by using principal component analysis and Horn’s parallel analysis. Furthermore, the influence of dimensionality reduction in global rigid image registration is investigated. It is shown that the registration results obtained from the reduced descriptor have the same high quality in comparison to those found for the original descriptor.

scholarly journals MULTI-FEATURE MUTUAL INFORMATION IMAGE REGISTRATION

2012 ◽  
Vol 31 (1) ◽  
pp. 43 ◽  
Author(s):  
Dejan Tomaževič ◽  
Boštjan Likar ◽  
Franjo Pernuš
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Spatial Information ◽  
Similarity Measures ◽  
Feature Space ◽  
Efficient Estimation ◽  
Mr Images ◽  
Feature Correspondence ◽  
Information Theoretic ◽  
Novel Approach

Nowadays, information-theoretic similarity measures, especially the mutual information and its derivatives, are one of the most frequently used measures of global intensity feature correspondence in image registration. Because the traditional mutual information similarity measure ignores the dependency of intensity values of neighboring image elements, registration based on mutual information is not robust in cases of low global intensity correspondence. Robustness can be improved by adding spatial information in the form of local intensity changes to the global intensity correspondence. This paper presents a novel method, by which intensities, together with spatial information, i.e., relations between neighboring image elements in the form of intensity gradients, are included in information-theoretic similarity measures. In contrast to a number of heuristic methods that include additional features into the generic mutual information measure, the proposed method strictly follows information theory under certain assumptions on feature probability distribution. The novel approach solves the problem of efficient estimation of multifeature mutual information from sparse high-dimensional feature space. The proposed measure was tested on magnetic resonance (MR) and computed tomography (CT) images. In addition, the measure was tested on positron emission tomography (PET) and MR images from the widely used Retrospective Image Registration Evaluation project image database. The results indicate that multi-feature mutual information, which combines image intensities and intensity gradients, is more robust than the standard single-feature intensity based mutual information, especially in cases of low global intensity correspondences, such as in PET/MR images or significant intensity inhomogeneity.

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