A Wavelet-Based Similarity Measure to register pre-/intra-operative MR images of the brain (original) (raw)
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IEEE Transactions on Information Technology in Biomedicine, 2002
In the field of template-based medical image analysis, image registration and normalization are frequently used to evaluate and interpret data in a standard template or reference atlas space. Despite the large number of image-registration (warping) techniques developed recently in the literature, only a few studies have been undertaken to numerically characterize and compare various alignment methods. In this paper, we introduce a new approach for analyzing image registration based on a selectivewavelet reconstruction technique using a frequency-adaptive wavelet shrinkage. We study four polynomial-based and two higher complexity nonaffine warping methods applied to groups of stereotaxic human brain structural (magnetic resonance imaging) and functional (positron emission tomography) data. Depending upon the aim of the image registration, we present several warp classification schemes. Our method uses a concise representation of the native and resliced (pre-and post-warp) data in compressed wavelet space to assess quality of registration. This technique is computationally inexpensive and utilizes the image compression, image enhancement, and denoising characteristics of the wavelet-based function representation, as well as the optimality properties of frequency-dependent wavelet shrinkage.
Department of Statistics Ucla, 2002
In the field of template-based medical image analysis, image registration and normalization are frequently used to evaluate and interpret data in a standard template or reference atlas space. Despite the large number of image-registration (warping) techniques developed recently in the literature, only a few studies have been undertaken to numerically characterize and compare various alignment methods. In this paper, we introduce a new approach for analyzing image registration based on a selectivewavelet reconstruction technique using a frequency-adaptive wavelet shrinkage. We study four polynomial-based and two higher complexity nonaffine warping methods applied to groups of stereotaxic human brain structural (magnetic resonance imaging) and functional (positron emission tomography) data. Depending upon the aim of the image registration, we present several warp classification schemes. Our method uses a concise representation of the native and resliced (pre-and post-warp) data in compressed wavelet space to assess quality of registration. This technique is computationally inexpensive and utilizes the image compression, image enhancement, and denoising characteristics of the wavelet-based function representation, as well as the optimality properties of frequency-dependent wavelet shrinkage.
Wavelet-Based Medical Image Registration for Retrieval Applications
2008 International Conference on BioMedical …, 2008
In this paper, a novel, fully automatic, multiscale wavelet-based image registration technique is proposed for image retrieval applications. We present a fast 2-D rigid registration scheme for retrieval applications. Here, the use of multiscale wavelet representation with mutual information (MI) is expected to facilitate matching of important anatomical structures at multiple resolutions. We propose to use a dyadic grid in the parameter search space for efficient computation in the multiscale domain. The proposed approach has several novel aspects including the use of MI in multiscale wavelet domain and variable bin sizes for each level of decomposition. The technique is also tested under varying noise levels. The results show high efficacy of the proposed approach.
An Efficient MRI Brain Image Registration and Wavelet Based Fusion
International Journal of Recent Technology and Engineering (IJRTE), 2019
Over last few decades, 3D reconstruction of medical images becomes advance technique in medical image processing. Reconstruction of 2D images of such data sets into 3D volumes, via registration of 2D sections had become a most interesting topic. In current years, MRI has been used for many medical analysis applications. The proposed system considered MRI images are taken from the same view, different times or acquired by different imaging modalities to increase the information. T1, T2 and PD MRI are taken as an input; T2 image is registered with a reference of T1 image using affine transformation, the registered T2 image is fused with T1 using DWT. The Fused T1T2 image is taken as reference image to register PD image using B-Spline transformation. DT-CWT technique is used to fuse the T1T2 image with registered PD image. The performance of the system shows that the proposed system gives more information by fusing T1T2PD images.
IRJET, 2022
Image registration is done prior to image fusion. Medical and biomedical images are taken as an example for CT images which rely on reference image. The estimated time and error rates are calculated based on the transformation of images. This paper introduces the calculation of performance quality metrics based on the pictures taken during the MRI and CT of a brain. The approach is to create a method for stiff CT and MRI image registration using wavelet image fusion.
A fast and accurate method to register medical images using Wavelet Modulus Maxima
Pattern Recognition Letters, 2000
This paper presents a fast, accurate and automatic method to register images of rigid bodies. It uses wavelets to obtain control points. Wavelets are not shift invariant but the structures determined by the wavelet high pass image, the Modulus Maxima Image, provide the information necessary for a fast-rough convergence. These structures represent shapes from which we segment the invariant shapes for the images being registered. For example, the MRI and CT images of the brain can be considered as rigid bodies that do not undergo a change in shape over reasonable periods of time. By using a convex hull, we make the procedure insensitive to the internal changes in the object. Hence, even with the growth of tumors, the procedure registers brain images very accurately. The method uses the correlation coecient to measure the similarity between images and to determine how well the images are registered. The method has been extensively tested with various types of images and in all cases the registration accuracy is very high. The correlation coecient used to validate the registrations has de®ciencies that occasionally required a visual inspection to terminate the algorithm after a point of marginal improvement. Ó
Wavelet Methods for Medical Image Registration
Registration of CT and MRI images with medical atlases showing location of anatomical structures that may not appear in the images is required for accurate diagnostics and e ective treatment. Registration maps computed as solutions of elliptic boundary value problems have proven e ective for two dimensional problems but conventional algorithms are too slow for clinical applications of three dimensional images. E cient wavelet methods have have been developed for the two dimensional Laplace equation. This paper extends these methods to three dimensions and to Navier's equation for linearized elasticity. Future research is also discussed.
IJERT-Area Based Image Registration using Wavelet Transform and Oriented Laplacian Pyramid
International Journal of Engineering Research and Technology (IJERT), 2015
https://www.ijert.org/area-based-image-registration-using-wavelet-transform-and-oriented-laplacian-pyramid https://www.ijert.org/research/area-based-image-registration-using-wavelet-transform-and-oriented-laplacian-pyramid-IJERTV4IS010370.pdf Many digital imaging techniques in medicine combine multiple images for analysis. Using these techniques, it is essential to align and register images prior to addition, subtraction, or any other combination of the images. An area based method for registration is proposed in this work. A technique for image registration using mutual information, cross correlation and mean squared difference has been developed. This technique consist of three main steps: extracting feature points in the reference and the sensed images, establishing the correspondence between the feature points of the images, and estimating the transformation parameters which map the target image to the reference one. Wavelet transform and oriented laplacian pyramid is used for feature extraction and features are matched using mutual information, cross correlation and mean squared difference. The obtained results of mutual information based method are compared with cross correlation and means squares techniques. The proposed algorithm is evaluated using several images brain images. Performance of these methods is evaluated using PSNR.
Evaluating similarity measures for brain image registration
Journal of Visual Communication and Image Representation, 2013
Evaluation of similarity measures for image registration is a challenging problem due to its complex interaction with the underlying optimization, regularization, image type and modality. We propose a single performance metric, named robustness, as part of a new evaluation method which quantifies the effectiveness of similarity measures for brain image registration while eliminating the effects of the other parts of the registration process. We show empirically that similarity measures with higher robustness are more effective in registering degraded images and are also more successful in performing intermodal image registration. Further, we introduce a new similarity measure, called normalized spatial mutual information, for 3D brain image registration whose robustness is shown to be much higher than the existing ones. Consequently, it tolerates greater image degradation and provides more consistent outcomes for intermodal brain image registration.