PCANet-Based Structural Representation for Nonrigid Multimodal Medical Image Registration

Zhu, Xingxing; Ding, Mingyue; Huang, Tao; Jin, Xiaomeng; Zhang, Xuming

doi:10.3390/s18051477

Cited by 25 publications

(17 citation statements)

References 35 publications

(33 reference statements)

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“…However, the non-rigid transformation often involves numerous parameters, which will render accurate image registration difficult [5,6,7,8]. Therefore, the non-rigid multi-modal 3D medical image registration has become a challenging task [9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent Neighborhood Descriptor

Yang

Ding

Zhang

2019

Sensors

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The non-rigid multi-modal three-dimensional (3D) medical image registration is highly challenging due to the difficulty in the construction of similarity measure and the solution of non-rigid transformation parameters. A novel structural representation based registration method is proposed to address these problems. Firstly, an improved modality independent neighborhood descriptor (MIND) that is based on the foveated nonlocal self-similarity is designed for the effective structural representations of 3D medical images to transform multi-modal image registration into mono-modal one. The sum of absolute differences between structural representations is computed as the similarity measure. Subsequently, the foveated MIND based spatial constraint is introduced into the Markov random field (MRF) optimization to reduce the number of transformation parameters and restrict the calculation of the energy function in the image region involving non-rigid deformation. Finally, the accurate and efficient 3D medical image registration is realized by minimizing the similarity measure based MRF energy function. Extensive experiments on 3D positron emission tomography (PET), computed tomography (CT), T1, T2, and (proton density) PD weighted magnetic resonance (MR) images with synthetic deformation demonstrate that the proposed method has higher computational efficiency and registration accuracy in terms of target registration error (TRE) than the registration methods that are based on the hybrid L-BFGS-B and cat swarm optimization (HLCSO), the sum of squared differences on entropy images, the MIND, and the self-similarity context (SSC) descriptor, except that it provides slightly bigger TRE than the HLCSO for CT-PET image registration. Experiments on real MR and ultrasound images with unknown deformation have also be done to demonstrate the practicality and superiority of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent Neighborhood Descriptor

Yang

Ding

Zhang

2019

Sensors

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“…Alternatively, motion estimation can be recast as a datadriven learning task (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18), which reduces processing times drastically because trained methods can quickly compute centering the images about the center of the left ventricle and cropping the resulting images to the size 80 3 80 3 16. For each subgroup with 30 participants, 20 were randomly chosen for training and the remaining 10 were used for testing, leaving 100 participants for training and 50 participants for testing.…”

mentioning

confidence: 99%

Implementation and Validation of a Three-dimensional Cardiac Motion Estimation Network

Morales¹,

Izquierdo‐Garcia²,

Aganj³

et al. 2019

Radiology: Artificial Intelligence

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To describe an unsupervised three-dimensional cardiac motion estimation network (CarMEN) for deformable motion estimation from two-dimensional cine MR images. Materials and Methods: A function was implemented using CarMEN, a convolutional neural network that takes two three-dimensional input volumes and outputs a motion field. A smoothness constraint was imposed on the field by regularizing the Frobenius norm of its Jacobian matrix. CarMEN was trained and tested with data from 150 cardiac patients who underwent MRI examinations and was validated on synthetic (n = 100) and pediatric (n = 33) datasets. CarMEN was compared to five state-of-the-art nonrigid body registration methods by using several performance metrics, including Dice similarity coefficient (DSC) and end-point error. Results: On the synthetic dataset, CarMEN achieved a median DSC of 0.85, which was higher than all five methods (minimummaximum median [or MMM], 0.67-0.84; P , .001), and a median end-point error of 1.7, which was lower than (MMM, 2.1-2.7; P , .001) or similar to (MMM, 1.6-1.7; P. .05) all other techniques. On the real datasets, CarMEN achieved a median DSC of 0.73 for Automated Cardiac Diagnosis Challenge data, which was higher than (MMM, 0.33; P , .0001) or similar to (MMM, 0.72-0.75; P. .05) all other methods, and a median DSC of 0.77 for pediatric data, which was higher than (MMM, 0.71-0.76; P , .0001) or similar to (MMM, 0.77-0.78; P. .05) all other methods. All P values were derived from pairwise testing. For all other metrics, CarMEN achieved better accuracy on all datasets than all other techniques except for one, which had the worst motion estimation accuracy. Conclusion: The proposed deep learning-based approach for three-dimensional cardiac motion estimation allowed the derivation of a motion model that balances motion characterization and image registration accuracy and achieved motion estimation accuracy comparable to or better than that of several state-of-the-art image registration algorithms.

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“…PCANet has been chosen as the main framework for several applications, including personal identification from ECG signal [49], traffic light recognition [50], remote sensing [51], medical image analysis [52], and automatic ship detection [53]. LDANet follows the same strategy used by PCANet and employs a similar architecture, with the difference that the filter banks used for convolution are obtained through the LDA basis vectors.…”

Section: Related Workmentioning

confidence: 99%

A semi-supervised convolutional neural network based on subspace representation for image classification

Gatto

Souza

Santos

et al. 2020

J Image Video Proc.

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This work presents a shallow network based on subspaces with applications in image classification. Recently, shallow networks based on PCA filter banks have been employed to solve many computer vision-related problems including texture classification, face recognition, and scene understanding. These approaches are robust, with a straightforward implementation that enables fast prototyping of practical applications. However, these architectures employ either unsupervised or supervised learning. As a result, they may not achieve highly discriminative features in more complicated computer vision problems containing variations in camera motion, object's appearance, pose, scale, and texture, due to drawbacks related to each learning paradigm. To cope with this disadvantage, we propose a semi-supervised shallow network equipped with both unsupervised and supervised filter banks, presenting representative and discriminative abilities. Besides, the introduced architecture is flexible, performing favorably on different applications whose amount of supervised data is an issue, making it an attractive choice in practice. The proposed network is evaluated on five datasets. The results show improvement in terms of prediction rate, comparing to current shallow networks.

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PCANet-Based Structural Representation for Nonrigid Multimodal Medical Image Registration

Cited by 25 publications

References 35 publications

Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent Neighborhood Descriptor

Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent Neighborhood Descriptor

Implementation and Validation of a Three-dimensional Cardiac Motion Estimation Network

A semi-supervised convolutional neural network based on subspace representation for image classification

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