A Spatial-Feature-Enhanced MMI Algorithm for Multimodal Airborne Image Registration

Fan, Xiaofeng; Rhody, Harvey; Saber, Eli

doi:10.1109/tgrs.2010.2040390

Cited by 40 publications

(2 citation statements)

References 24 publications

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“…The study in [7] combined the Harris corners as the feature information to enhance the conventional MMI registration. A registration method based on multiresolution generic algorithms [8] for 3D registration of cardiac PET/CT images achieved a high accuracy in a reasonable time.…”

Section: Introductionmentioning

confidence: 99%

A Registration Method Based on Contour Point Cloud for 3D Whole-Body PET and CT Images

Song

Jiang

Yang

et al. 2017

BioMed Research International

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The PET and CT fusion image, combining the anatomical and functional information, has important clinical meaning. An effective registration of PET and CT images is the basis of image fusion. This paper presents a multithread registration method based on contour point cloud for 3D whole-body PET and CT images. Firstly, a geometric feature-based segmentation (GFS) method and a dynamic threshold denoising (DTD) method are creatively proposed to preprocess CT and PET images, respectively. Next, a new automated trunk slices extraction method is presented for extracting feature point clouds. Finally, the multithread Iterative Closet Point is adopted to drive an affine transform. We compare our method with a multiresolution registration method based on Mattes Mutual Information on 13 pairs (246~286 slices per pair) of 3D whole-body PET and CT data. Experimental results demonstrate the registration effectiveness of our method with lower negative normalization correlation (NC = −0.933) on feature images and less Euclidean distance error (ED = 2.826) on landmark points, outperforming the source data (NC = −0.496, ED = 25.847) and the compared method (NC = −0.614, ED = 16.085). Moreover, our method is about ten times faster than the compared one.

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Section: Introductionmentioning

confidence: 99%

A Registration Method Based on Contour Point Cloud for 3D Whole-Body PET and CT Images

Song

Jiang

Yang

et al. 2017

BioMed Research International

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“…Sampling of important points is carried out by the curvelet tool but in distinct and i.i.d form. Instead of estimating the joint distribution in the intensity space, some other researchers tried to use higher dimensions along with other parameters such as the angle between GVF vectors [14], edginess [6] and other features of the points. It is clear that the mentioned method is also very time-consuming due to its numerous computations in each iteration of optimization loop.…”

Section: Introductionmentioning

confidence: 99%

Structural image representation for image registration

Aghajani

Shirpour

Manzuri

2015

2015 the International Symposium on Artificial Intelligence and Signal Processing (AISP)

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Image registration is an important task in medical image processing. Assuming spatially stationary intensity relation among images, conventional area based algorithms such as CC (Correlation Coefficients) and MI (Mutual Information), show weaker results alongside spatially varying intensity distortion. In this research, a structural representation of images is introduced. It allows us to use simpler similarity metrics in multimodal images which are also robust against the mentioned distortion field. The efficiency of this presentation in non-rigid image registration in the presence of spatial varying distortion field is examined. Experimental results on synthetic and real-world data sets demonstrate the effectiveness of the proposed method for image registration tasks.

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A Bimodal Co-sparse Analysis Model for Image Processing

et al. 2014

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The success of many computer vision tasks lies in the ability to exploit the interdependency between different image modalities such as intensity and depth. Fusing corresponding information can be achieved on several levels, and one promising approach is the integration at a low level. Moreover, sparse signal models have successfully been used in many vision applications. Within this area of research, the socalled co-sparse analysis model has attracted considerably less attention than its well-known counterpart, the sparse synthesis model, although it has been proven to be very useful in various image processing applications.In this paper, we propose a co-sparse analysis model that is able to capture the interdependency of two image modalities. It is based on the assumption that a pair of analysis operators exists, so that the co-supports of the corresponding bimodal image structures are correlated. We propose an algorithm that is able to learn such a coupled pair of operators from registered and noise-free training data. Furthermore, we explain how this model can be applied to solve linear inverse problems in image processing and how it can be used for image registration tasks. This paper extends the work of some of the authors by two major contributions. Firstly, a modification of the learning process is proposed that a priori guarantees unit norm and zero-mean of the rows of the operator. This accounts for the intuition that contrast in image modalities carries the most information. Secondly, the model is used in a novel bimodal image registration algorithm which estimates the transformation parameters of unregistered images of different modalities.

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A Spatial-Feature-Enhanced MMI Algorithm for Multimodal Airborne Image Registration

Cited by 40 publications

References 24 publications

A Registration Method Based on Contour Point Cloud for 3D Whole-Body PET and CT Images

A Registration Method Based on Contour Point Cloud for 3D Whole-Body PET and CT Images

Structural image representation for image registration

A Bimodal Co-sparse Analysis Model for Image Processing

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