An Abdominal Registration Technology for Integration of Nanomaterial Imaging-Aided Diagnosis and Treatment

2021 ◽  
Vol 17 (5) ◽  
pp. 952-959
Author(s):  
Shao-Di Yang ◽  
Yu-Qian Zhao ◽  
Fan Zhang ◽  
Miao Liao ◽  
Zhen Yang ◽  
...  
Keyword(s):  
Image Registration ◽  
Unsupervised Learning ◽  
Large Scale ◽  
Three Dimensional ◽  
Initial Step ◽  
Abdominal Ct ◽  
Registration Method ◽  
Therapy Effect ◽  
Coarse To Fine ◽  
Aided Diagnosis

Image registration technology is a key technology used in the process of nanomaterial imaging-aided diagnosis and targeted therapy effect monitoring for abdominal diseases. Recently, the deep-learning based methods have been increasingly used for large-scale medical image registration, because their iteration is much less than those of traditional ones. In this paper, a coarse-to-fine unsupervised learning-based three-dimensional (3D) abdominal CT image registration method is presented. Firstly, an affine transformation was used as an initial step to deal with large deformation between two images. Secondly, an unsupervised total loss function containing similarity, smoothness, and topology preservation measures was proposed to achieve better registration performances during convolutional neural network (CNN) training and testing. The experimental results demonstrated that the proposed method severally obtains the average MSE, PSNR, and SSIM values of 0.0055, 22.7950, and 0.8241, which outperformed some existing traditional and unsupervised learning-based methods. Moreover, our method can register 3D abdominal CT images with shortest time and is expected to become a real-time method for clinical application.

A Coarse-to-Fine Registration on 3D Multi-Phase Abdominal CT Images

2020 ◽  
Vol 12 (7) ◽  
pp. 909-914
Author(s):  
Shao-Di Yang ◽  
Fan Zhang ◽  
Zhen Yang ◽  
Xiao-Yu Yang ◽  
Shu-Zhou Li
Keyword(s):  
Three Dimensional ◽  
Ct Images ◽  
Abdominal Ct ◽  
Registration Method ◽  
Coarse Registration ◽  
Multi Phase ◽  
Coarse To Fine ◽  
Aided Diagnosis ◽  
Fine Registration ◽  
Spatial Regularization

Registration is a technical support for the integration of nanomaterial imaging-aided diagnosis and treatment. In this paper, a coarse-to-fine three-dimensional (3D) multi-phase abdominal CT images registration method is proposed. Firstly, a linear model is used to coarsely register the paired multiphase images. Secondly, an intensity-based registration framework is proposed, which contains the data and spatial regularization terms and performs fine registration on the paired images obtained in the coarse registration step. The results illustrate that the proposed method is superior to some existing methods with the average MSE, PSNR, and SSIM values of 0.0082, 21.2695, and 0.8956, respectively. Therefore, the proposed method provides an efficient and robust framework for 3D multi-phase abdominal CT images registration.

scholarly journals An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6254
Author(s):  
Shaodi Yang ◽  
Yuqian Zhao ◽  
Miao Liao ◽  
Fan Zhang
Keyword(s):  
Computed Tomography ◽  
Unsupervised Learning ◽  
Medical Image ◽  
Ct Images ◽  
Abdominal Ct ◽  
Registration Method ◽  
Spatial Consistency ◽  
Net Framework ◽  
Organ Registration ◽  
Coarse To Fine

Medical image registration is an essential technique to achieve spatial consistency geometric positions of different medical images obtained from single- or multi-sensor, such as computed tomography (CT), magnetic resonance (MR), and ultrasound (US) images. In this paper, an improved unsupervised learning-based framework is proposed for multi-organ registration on 3D abdominal CT images. First, the explored coarse-to-fine recursive cascaded network (RCN) modules are embedded into a basic U-net framework to achieve more accurate multi-organ registration results from 3D abdominal CT images. Then, a topology-preserving loss is added in the total loss function to avoid a distortion of the predicted transformation field. Four public databases are selected to validate the registration performances of the proposed method. The experimental results show that the proposed method is superior to some existing traditional and deep learning-based methods and is promising to meet the real-time and high-precision clinical registration requirements of 3D abdominal CT images.

scholarly journals A Novel Coarse-to-Fine Scheme for Remote Sensing Image Registration Based on SIFT and Phase Correlation

2019 ◽  
Vol 11 (15) ◽  
pp. 1833 ◽  
Author(s):  
Han Yang ◽  
Xiaorun Li ◽  
Liaoying Zhao ◽  
Shuhan Chen
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Phase Correlation ◽  
Mean Square ◽  
Extended Phase ◽  
Registration Method ◽  
Coarse Registration ◽  
Feature Based ◽  
Coarse To Fine ◽  
Fine Registration

Automatic image registration has been wildly used in remote sensing applications. However, the feature-based registration method is sometimes inaccurate and unstable for images with large scale difference, grayscale and texture differences. In this manuscript, a coarse-to-fine registration scheme is proposed, which combines the advantage of feature-based registration and phase correlation-based registration. The scheme consists of four steps. First, feature-based registration method is adopted for coarse registration. A geometrical outlier removal method is applied to improve the accuracy of coarse registration, which uses geometric similarities of inliers. Then, the sensed image is modified through the coarse registration result under affine deformation model. After that, the modified sensed image is registered to the reference image by extended phase correlation. Lastly, the final registration results are calculated by the fusion of the coarse registration and the fine registration. High universality of feature-based registration and high accuracy of extended phase correlation-based registration are both preserved in the proposed method. Experimental results of several different remote sensing images, which come from several published image registration papers, demonstrate the high robustness and accuracy of the proposed method. The evaluation contains root mean square error (RMSE), Laplace mean square error (LMSE) and red–green image registration results.

scholarly journals A Methodology of Three-Dimensional Medical Image Registration Based on Conformal Geometric Invariant

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Liang Hua ◽  
Kean Yu ◽  
Lijun Ding ◽  
Juping Gu ◽  
Xinsong Zhang ◽  
...  
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Subjective Evaluation ◽  
Three Dimensional ◽  
Medical Image Registration ◽  
Geometric Invariant ◽  
Registration Accuracy ◽  
Registration Method ◽  
Cloud Data ◽  
Dual Vector

A three-dimensional multimodality medical image registration method using geometric invariant based on conformal geometric algebra (CGA) theory is put forward for responding to challenges resulting from many free degrees and computational burdens with 3D medical image registration problems. The mathematical model and calculation method of dual-vector projection invariant are established using the distribution characteristics of point cloud data and the point-to-plane distance-based measurement in CGA space. The translation operator and geometric rotation operator during registration operation are built in Clifford algebra (CA) space. The conformal geometrical algebra is used to realize the registration of 3D CT/MR-PD medical image data based on the dual vector geometric invariant. The registration experiment results indicate that the methodology proposed in this paper is of stronger commonality, less computation burden, shorter time consumption, and intuitive geometric meaning. Both subjective evaluation and objective indicators show that the methodology proposed here is of high registration accuracy and suitable for 3D medical image registration.

A Three-Dimensional Registration Method for MicroUS/MicroPET Multimodality Small-Animal Imaging

2007 ◽  
Vol 29 (3) ◽  
pp. 155-166 ◽  
Author(s):  
Ai-Ho Liao ◽  
Li-Yen Chen ◽  
Wen-Fang Cheng ◽  
Pai-Chi Li
Keyword(s):  
Image Registration ◽  
Structural Information ◽  
Multimodality Imaging ◽  
Imaging Techniques ◽  
Small Animal Imaging ◽  
Three Dimensional ◽  
Small Animal ◽  
Imaging Method ◽  
Registration Method ◽  
Animal Imaging

Small-animal models are used extensively in disease research, genomics research, drug development and developmental biology. The development of noninvasive small-animal imaging techniques with adequate spatial resolution and sensitivity is therefore of prime importance. In particular, multimodality small-animal imaging can provide complementary information. This paper presents a method for registering high-frequency ultrasonic (microUS) images with small-animal positron-emission tomography (microPET) images. Registration is performed using six external multimodality markers, each being a glass bead with a diameter of 0.43–0.60 mm, with 0.1 μl of [18F]FDG placed in each marker holder. A small-animal holder is used to transfer mice between the microPET and microUS systems. Multimodality imaging was performed on C57BL/6J black mice bearing WF-3 ovary cancer cells in the second week after tumor implantation and rigid-body image registration of the six markers was also performed. The average registration error was 0.31 mm when all six markers were used and increased as the number of markers decreased. After image registration, image segmentation and fusion are performed on the tumor. Our multimodality small-animal imaging method allows structural information from microUS to be combined with functional information from microPET, with the preliminary results showing it to be an effective tool for cancer research.

scholarly journals PathFlow-MixMatch for Whole Slide Image Registration: An Investigation of a Segment-Based Scalable Image Registration Method

2020 ◽  
Author(s):  
Joshua J. Levy ◽  
Christopher R. Jackson ◽  
Christian C. Haudenschild ◽  
Brock C. Christensen ◽  
Louis J. Vaickus
Keyword(s):  
Image Registration ◽  
Large Scale ◽  
Digital Pathology ◽  
Superior Performance ◽  
Registration Method ◽  
Target Registration Error ◽  
Alignment Algorithms ◽  
Slide Image ◽  
Segment Matching ◽  
The Impact

AbstractImage registration involves finding the best alignment between different images of the same object. In these tasks, the object in question is viewed differently in each of the images (e.g. different rotation or light conditions, etc.). In digital pathology, image registration aligns correspondent regions of tissue from different stereotactic viewpoints (e.g. subsequent deeper sections of the same tissue). These comparisons are important for histological analysis and can facilitate previously unavailable manipulations, such as 3D tissue reconstruction and cell-level alignment of immunohistochemical (IHC) and special stains. Several benchmarks have been established for evaluating image registration techniques for histological tissue; however, little work has evaluated the impact of scaling registration techniques to Giga-Pixel Whole Slide Images (WSI), which are large enough for significant memory limitations, and contain recurrent patterns and deformations that hinder traditional alignment algorithms. Furthermore, as tissue sections often contain multiple, discrete, smaller tissue fragments, it is unnecessary to align an entire image when the bulk of the image is background whitespace and tissue fragments’ orientations are often agnostic of each other. We present a methodology for circumventing large-scale image registration issues in histopathology and accompanying software. By removing background pixels, parsing the slide into discrete tissue segments, and matching, orienting and registering smaller segment pairs, we recovered registrations with lower Target Registration Error (TRE) when compared to utilizing the unmanipulated WSI. We tested our technique by having a pathologist annotate landmarks from 13 pairs of differently stained liver biopsy slides, performing WSI and segment-based registration techniques, and comparing overall TRE. Preliminary results demonstrate superior performance of registering segment pairs versus registering WSI (difference of median TRE of 44 pixels, p<0.001). Segment matching within WSI is an effective solution for histology image registration but requires further testing and validation to ensure its viability for stain translation and 3D histology analysis.

Local Character Tensors for 3D Registration Method on Free-View Datasets

2007 ◽  
Vol 11 (7) ◽  
pp. 848-857
Author(s):  
Jingjing Wang ◽  
◽  
Fangyan Dong ◽  
Yutaka Hatakeyama ◽  
Hajime Nobuhara ◽  
...  
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
Geometric Object ◽  
Computational Time ◽  
Object Model ◽  
3D Registration ◽  
Registration Method ◽  
Local Character ◽  
Optimal Segmentation ◽  
Matching Probability

A local character tensor is proposed for the automatic three-dimensional (3D) pair-wise registration based on free-view 3D datasets. In the proposed method, there are two characters, i.e., the optimal segmentation to realize the automatic processing and local character tensor to improve the matching probability. It is applied for solving the mismatching problem and large-scale 3D datasets, using non-structured datasets are tested in a PC with Intel Pentium M 1.50 GHz and 1.0 GB memory. Pair-wised experimental results show the proposed method increases average 12.6% matching probability and decreases average 18.9 seconds computational time compared to the conventional local character based registration method. This registration method can be further applied to 3D reconstruction from navigation, model based object recognition to accurate 3D geometric object model application.

A coarse-to-fine registration method for three-dimensional MR images

2021 ◽  
Vol 59 (2) ◽  
pp. 457-469
Author(s):  
Cuixia Li ◽  
Yuanyuan Zhou ◽  
Yinghao Li ◽  
Shanshan Yang
Keyword(s):  
Three Dimensional ◽  
Mr Images ◽  
Registration Method ◽  
Coarse To Fine ◽  
Fine Registration

scholarly journals An Orthopedic Auxiliary Diagnosis System Based on Image Recognition Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yang Guo ◽  
Chen Chen
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Computer Aided Diagnosis ◽  
Cartilage Tissue ◽  
Two Dimensional ◽  
Registration Method ◽  
Diagnosis System ◽  
Computer Aided ◽  
Orthopedic Diseases ◽  
Aided Diagnosis

There are many kinds of orthopedic diseases with complex professional background, and it is easy to miss diagnosis and misdiagnosis. The computer-aided diagnosis system of orthopedic diseases based on the key technology of medical image processing can locate and display the lesion location area by visualization, measuring and providing disease diagnosis indexes. It is of great significance to assist orthopedic doctors to diagnose orthopedic diseases from the perspective of visual vision and quantitative indicators, which can improve the diagnosis rate and accuracy of orthopedic diseases, reduce the pain of patients, and shorten the treatment time of diseases. To solve the problem of possible spatial inconsistency of medical images of orthopedic diseases, we propose an image registration method based on volume feature point selection and Powell. Through the linear search strategy of golden section method and Powell algorithm optimization, the best spatial transformation parameters are found, which maximizes the normalized mutual information between images to be registered, thus ensuring the consistency of two-dimensional spatial positions. According to the proposed algorithm, a computer-aided diagnosis system of orthopedic diseases is developed and designed independently. The system consists of five modules, which can complete many functions such as medical image input and output, algorithm processing, and effect display. The experimental results show that the system developed in this paper has good results in cartilage tissue segmentation, bone and urate agglomeration segmentation, urate agglomeration artifact removal, two-dimensional and three-dimensional image registration, and visualization. The system can be applied to clinical gout and cartilage defect diagnosis and evaluation, providing sufficient basis to assist doctors in making diagnosis decisions.

scholarly journals Automatic 3D image registration for nano-resolution chemical mapping using synchrotron spectro-tomography

2021 ◽  
Vol 28 (1) ◽  
pp. 278-282
Author(s):  
Jin Zhang ◽  
Jun Hu ◽  
Zhisen Jiang ◽  
Kai Zhang ◽  
Peng Liu ◽  
...  
Keyword(s):  
Image Registration ◽  
Energy Levels ◽  
Three Dimensional ◽  
Intensity Variation ◽  
Chemical Mapping ◽  
3D Image ◽  
Experimental Conditions ◽  
Registration Method ◽  
X Ray ◽  
3D Image Registration

Nano-resolution synchrotron X-ray spectro-tomography has been demonstrated as a powerful tool for probing the three-dimensional (3D) structural and chemical heterogeneity of a sample. By reconstructing a number of tomographic data sets recorded at different X-ray energy levels, the energy-dependent intensity variation in every given voxel fingerprints the corresponding local chemistry. The resolution and accuracy of this method, however, could be jeopardized by non-ideal experimental conditions, e.g. instability in the hardware system and/or in the sample itself. Herein is presented one such case, in which unanticipated sample deformation severely degrades the data quality. To address this issue, an automatic 3D image registration method is implemented to evaluate and correct this effect. The method allows the redox heterogeneity in partially delithiated Li x Ta0.3Mn0.4O2 battery cathode particles to be revealed with significantly improved fidelity.

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