Medical Image Registration via Similarity Measure based on Convolutional Neural Network

2020 ◽  
Author(s):  
Li Dong ◽  
Yongzheng Lin ◽  
Yishen Pang
Keyword(s):  
Neural Network ◽  
Image Registration ◽  
Convolutional Neural Network ◽  
Similarity Measure ◽  
Medical Image ◽  
Medical Image Registration

Unsupervised end-to-end Brain Tumor Magnetic Resonance Image Registration using RBCNN: Rigid Transformation, B-Spline Transformation and Convolutional Neural Network

2021 ◽  
Vol 17 ◽  
Author(s):  
Senthil Pandi Sankareswaran ◽  
Mahadevan Krishnan
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Image Registration ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Computer Assisted ◽  
Rigid Transformation ◽  
Medical Image Registration ◽  
B Spline

Background: Image registration is the process of aligning two or more images in a single coordinate. Now a days, medical image registration plays a significant role in computer assisted disease diagnosis, treatment, and surgery. The different modalities available in the medical image makes medical image registration as an essential step in Computer Assisted Diagnosis(CAD), Computer-Aided Therapy (CAT) and Computer-Assisted Surgery (CAS). Problem definition: Recently many learning based methods were employed for disease detection and classification but those methods were not suitable for real time due to delayed response and need of pre alignment,labeling. Method: The proposed research constructed a deep learning model with Rigid transform and B-Spline transform for medical image registration for an automatic brain tumour finding. The proposed research consists of two steps. First steps uses Rigid transformation based Convolutional Neural Network and the second step uses B-Spline transform based Convolutional Neural Network. The model is trained and tested with 3624 MR (Magnetic Resonance) images to assess the performance. The researchers believe that MR images helps in success the treatment of brain tumour people. Result: The result of the proposed method is compared with the Rigid Convolutional Neural Network (CNN), Rigid CNN + Thin-Plat Spline (TPS), Affine CNN, Voxel morph, ADMIR (Affine and Deformable Medical Image Registration) and ANT(Advanced Normalization Tools) using DICE score, average symmetric surface distance (ASD), and Hausdorff distance. Conclusion: The RBCNN model will help the physician to automatically detect and classify the brain tumor quickly(18 Sec) and efficiently with out doing any pre-alignment and labeling.

Rigid medical image registration using PCA neural network

2006 ◽  
Vol 69 (13-15) ◽  
pp. 1717-1722 ◽  
Author(s):  
Lifeng Shang ◽  
Jian Cheng Lv ◽  
Zhang Yi
Keyword(s):  
Neural Network ◽  
Image Registration ◽  
Medical Image ◽  
Medical Image Registration

scholarly journals Bayesian Fully Convolutional Networks for Brain Image Registration

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kunpeng Cui ◽  
Panpan Fu ◽  
Yinghao Li ◽  
Yusong Lin
Keyword(s):  
Neural Network ◽  
Image Registration ◽  
Medical Image ◽  
Medical Image Registration ◽  
Bayesian Neural Network ◽  
Registration Accuracy ◽  
Change Analysis ◽  
Geometric Transformations ◽  
Convolutional Networks ◽  
Fully Convolutional Networks

The purpose of medical image registration is to find geometric transformations that align two medical images so that the corresponding voxels on two images are spatially consistent. Nonrigid medical image registration is a key step in medical image processing, such as image comparison, data fusion, target recognition, and pathological change analysis. Existing registration methods only consider registration accuracy but largely neglect the uncertainty of registration results. In this work, a method based on the Bayesian fully convolutional neural network is proposed for nonrigid medical image registration. The proposed method can generate a geometric uncertainty map to calculate the uncertainty of registration results. This uncertainty can be interpreted as a confidence interval, which is essential for judging whether the source data are abnormal. Moreover, the proposed method introduces group normalization, which is conducive to the network convergence of the Bayesian neural network. Some representative learning-based image registration methods are compared with the proposed method on different image datasets. Experimental results show that the registration accuracy of the proposed method is better than that of the methods, and its antifolding performance is comparable to that of fast image registration and VoxelMorph. Furthermore, the proposed method can evaluate the uncertainty of registration results.

Medical image registration using unsupervised deep neural network: A scoping literature review

2022 ◽  
Vol 73 ◽  
pp. 103444
Author(s):  
Samaneh Abbasi ◽  
Meysam Tavakoli ◽  
Hamid Reza Boveiri ◽  
Mohammad Amin Mosleh Shirazi ◽  
Raouf Khayami ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Registration ◽  
Literature Review ◽  
Medical Image ◽  
Deep Neural Network ◽  
Medical Image Registration ◽  
Scoping Literature Review

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

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4675 ◽  
Author(s):  
Feng Yang ◽  
Mingyue Ding ◽  
Xuming Zhang
Keyword(s):  
Image Registration ◽  
Similarity Measure ◽  
Energy Function ◽  
Medical Image ◽  
Medical Image Registration ◽  
Self Similarity ◽  
Registration Accuracy ◽  
Registration Method ◽  
Rigid Deformation ◽  
Transformation Parameters

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 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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