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SuperFusion: A Versatile image registration and Fusion Network with Semantic Awareness

IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2121-2137页

作者： Linfeng Tang Yuxin Deng Yong Ma Jun Huang Jiayi Ma Electronic Information School Wuhan UniversityWuhan 430072China IEEE

image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition,the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision *** study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named Super Fusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion,and semantic segmentation tasks demonstrate the superiority of our Super Fusion compared to the state-of-the-art *** source code and pre-trained model are publicly available at https://***/Linfeng-Tang/Super Fusion.

关键词： Global spatial attention image fusion image registration mutual promotion semantic awareness

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Two fully automated data-driven 3D whole-breast segmentation strategies in MRI for MR-based breast density using image registration and U-Net with a focus on reproducibility

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Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 303-314页

作者： Jia Ying Renee Cattell Tianyun Zhao Lan Lei Zhao Jiang Shahid M.Hussain Yi Gao H‑H.Sherry Chow Alison T.Stopeck Patricia A.Thompson Chuan Huang Department of Biomedical Engineering Stony Brook UniversityStony BrookNY 11794USA Department of Radiation Oncology Renaissance School of MedicineStony Brook UniversityStony BrookNY 11794USA Department of Medicine Northside Hospital GwinnettLawrencevilleGA 30046USA Program of Public Health Renaissance School of MedicineStony Brook UniversityStony BrookNY 11794USA Department of Radiology Renaissance School of MedicineStony Brook UniversityStony BrookNY 11794USA Department of Biomedical Informatics Renaissance School of MedicineStony Brook UniversityStony BrookNY 11794USA School of Biomedical Engineering Health Science CenterShenzhen UniversityShenzhen 518060China University of Arizona Cancer Center TucsonAZ 85719USA Department of Medicine Renaissance School of MedicineStony Brook UniversityStony BrookNY 11794USA Stony Brook Cancer Center Stony Brook UniversityStony BrookNY 11794USA Department of Medicine Cedar Sinai CancerCedars Sinai Medical CenterLos AngelesCA 90048USA

Presence of higher breast density(BD)and persistence over time are risk factors for breast cancer.A quantitatively accurate and highly reproducible BD measure that relies on precise and reproducible whole-breast segmentation is *** this study,we aimed to develop a highly reproducible and accurate whole-breast segmentation algorithm for the generation of reproducible BD *** datasets of volunteers from two clinical trials were *** MR images were acquired on 3T Siemens Biograph mMR,Prisma,and Skyra using 3D Cartesian six-echo GRE sequences with a fat-water separation *** whole-breast segmentation strategies,utiliz-ing image registration and 3D U-Net,were *** segmentation was performed.A task-based analysis was performed:a previously developed MR-based BD measure,MagDensity,was calculated and assessed using automated and manual *** mean squared error(MSE)and intraclass correlation coefficient(ICC)between MagDensity were evaluated using the manual segmentation as a *** test-retest reproducibility of MagDensity derived from different breast segmentation methods was assessed using the difference between the test and retest measures(Δ_(2-1)),MSE,and *** results showed that MagDensity derived by the registration and deep learning segmentation methods exhibited high concordance with manual segmentation,with ICCs of 0.986(95%CI:0.974-0.993)and 0.983(95%CI:0.961-0.992),*** test-retest analysis,MagDensity derived using the regis-tration algorithm achieved the smallest MSE of 0.370 and highest ICC of 0.993(95%CI:0.982-0.997)when compared to other segmentation *** conclusion,the proposed registration and deep learning whole-breast segmentation methods are accurate and reliable for estimating *** methods outperformed a previously developed algorithm and manual segmentation in the test-retest assessment,with the registration exhibiting superior performance for highly reproducibl

关键词： Breast cancer Breast density Breast segmentation image registration Deep learning

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image registration Using a Complete Polar Transform

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Chinese Journal of Electronics 2014年第2期23卷 403-408页

作者： ZHAO Wei HUANG Lidong WANG Jun SUN Zebin Institute of Electronic and Information Engineering Beihang University

image registration is widely used in image processing. Researchers have introduced image registration techniques based on the log-polar transform for its rotation and scale invariant properties. It suffers from nonuniform sampling which makes the registration results susceptible to interference. To address the problems of traditional log-polar transform, a Complete polar transform(CPT) method is proposed, which samples the image evenly to preserve the whole information of original *** innovative pro jection transform is applied after CPT to obtain the rotation invariant property. We pre-matched feature points using the Scale invariant feature transform(SIFT)algorithm and re-matched them based on CPT to improve matching speed and accuracy. Experimental results show that the proposed method is accurate and robust to noise and alteration.

关键词： image registration Polar transform Feature points match Scale invariant feature transform(SIFT)

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image registration Technique for Assessing the Accuracy of Intraoperative Osteotomy for Pelvic Tumors by 3D-Printed Patient-Specific Templates

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Journal of Shanghai Jiaotong university(Science) 2021年第3期26卷 306-311页

作者：曲扬严孟宁李小敏吴兵柳思宇王燎武文艾松涛 Department of Radiology Shanghai Jiao Tong University School of MedicineShanghai200011China Shanghai Key Laboratory of Orthopaedic Implants Department of OrthopaedicsShanghai Ninth People’s HospitalShanghai Jiao Tong University School of MedicineShanghai200011China

The study aimed to explore the feasibility of an image registration technique for assessing the accuracy of intraoperative osteotomy of pelvic tumors by 3-dimensional(3D)-printed patient-specific *** with malignant pelvic tumors who were admitted to our hospital between March 2014 and December 2020 were retrospectively *** underwent hemi-pelvic resection and reconstruction by 3D-printed individualized *** registration between the designed model and the postoperative segmented model of the prosthesis was used to obtain the intraoperative osteotomy plane and reduce metal artifacts in postoperative computed tomography(CT)*** distance and angle between the planned and actual osteotomy planes were then used to assess the accuracy of the intraoperative *** patients with 13 osteotomy planes were enrolled,including four males and four *** median age at the time of imaging examination was 44 years(range,33–54 years).All intraoperative osteotomy planes were assessed *** mean distance between the planned and true intraoperative osteotomy planes was−0.69 cm(−7.5–7.35 cm),and the mean angle was 6.57°±3.36°(1.05°–11.88°).This new assessment method of registering the designed model and the postoperative CT segmented model of the prosthesis may be used to assess the accuracy of intraoperative osteotomy for pelvic tumors,using 3D printed patient-specific templates.

关键词： pelvic tumor image registration 3D-printed patient-specific template intraoperative osteotomy

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Phase Correlation Based Iris image registration Model

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Journal of Computer Science & Technology 2005年第3期20卷 419-425页

作者： Jun-ZhouHuang Tie-NiuTan LiMa Yun-HongWang NationalLaboratoryofPatternRecognition InstituteofAutomationChineseAcademyofSciencesBeijing100080P.R.China NationalLaboratoryofPatternRecognition InstituteofAutomationChineseAcademyofSciencesBeijing100080P.R.China／／SchoolofComputingBeihangUniversityBeijing100083P.R.China

Iris recognition is one of the most reliable personal identification methods. In iris recognition systems, image registration is an important component. Accurately registering iris images leads to higher recognition rate for an iris recognition system. This paper proposes a phase correlation based method for iris image registration with sub-pixel accuracy. Compared with existing methods, it is insensitive to image intensity and can compensate to a certain extent the non-linear iris deformation caused by pupil movement. Experimental results show that the proposed algorithm has an encouraging performance.

关键词： phase correlation image registration iris recognition biometrics

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Fast Remote-Sensing image registration Using Priori Information and Robust Feature Extraction

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Tsinghua Science and Technology 2016年第5期21卷 552-560页

作者： Xijia Liu Xiaoming Tao Ning Ge Department of Electronic Engineering Tsinghua University Beijing 100084 China

In this paper, we propose a fast registration scheme for remote-sensing images for use as a fundamental technique in large-scale online remote-sensing data processing tasks. First, we introduce priori-information images,and use machine learning techniques to identify robust remote-sensing image features from state-of-the-art ScaleInvariant Feature Transform（SIFT） features. Next, we apply a hierarchical coarse-to-fine feature matching and image registration scheme on the basis of additional priori information, including a robust feature location map and platform imaging parameters. Numerical simulation results show that the proposed scheme increases position repetitiveness by 34%, and can speed up the overall image registration procedure by a factor of 7：47 while maintaining the accuracy of the image registration performance.

关键词： remote sensing image registration priori information feature extraction

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Total Variation Constrained Non-Negative Matrix Factorization for Medical image registration

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IEEE/CAA Journal of Automatica Sinica 2021年第5期8卷 1025-1037页

作者： Chengcai Leng Hai Zhang Guorong Cai Zhen Chen Anup Basu the School of Mathematics Northwest UniversityXi’an 710127 the Institute of Automation Chinese Academy of SciencesBeijing 100190China the Department of Computing Science University of AlbertaEdmontonAB T6G 2E8Canada the College of Computer Engineering Jimei UniversityXiamen 361021China the School of Measuring and Optical Engineering Nanchang Hangkong UniversityNanchang 330063China

This paper presents a novel medical image registration algorithm named total variation constrained graphregularization for non-negative matrix factorization(TV-GNMF).The method utilizes non-negative matrix factorization by total variation constraint and graph *** main contributions of our work are the ***,total variation is incorporated into NMF to control the diffusion *** purpose is to denoise in smooth regions and preserve features or details of the data in edge regions by using a diffusion coefficient based on gradient ***,we add graph regularization into NMF to reveal intrinsic geometry and structure information of features to enhance the discrimination ***,the multiplicative update rules and proof of convergence of the TV-GNMF algorithm are *** conducted on datasets show that the proposed TV-GNMF method outperforms other state-of-the-art algorithms.

关键词： Data clustering dimension reduction image registration non-negative matrix factorization(NMF) total variation(TV)

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Applying rotation-invariant star descriptor to deep-sky image registration

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Frontiers of Computer Science 2018年第5期12卷 1013-1025页

作者： Haiyang ZHOU Yunzhi YU College of Optical Science and Engineering Zhejiang University Hangzhou 310027 China Department of Computer Science University of Southern California Los Angeles CA 90089-0781 USA

image registration is a critical process of many deep-sky image processing applications. image registration methods include image stacking to reduce noise or achieve long exposure effects within a short exposure time, image stitching to extend the field of view, and atmospheric turbu- lence removal. The most widely used method for deep-sky image registration is the triangle- or polygon-based method, which is both memory and computation intensive. Deepsky image registration mainly focuses on translation and rotation caused by the vibration of imaging devices and the Earth＇s rotation, where rotation is the more difficult problem. For this problem, the best method is to find corresponding rotation-invariant features between different images. In this paper, we analyze the defects introduced by applying rotation-invariant feature descriptors to deep-sky image reg- istration and propose a novel descriptor. First, a dominant orientation is estimated from the geometrical relationships between a described star and two neighboring stable stars. An adaptive speeded-up robust features （SURF） descriptor is then constructed. During the construction of SURF, the local patch size adaptively changes based on the described star size. Finally, the proposed descriptor is formed by fusing star properties, geometrical relationships, and the adaptive SURF. Extensive experiments demonstrate that the proposed descriptor successfully addresses the gap resulting from applying the traditional feature-based method to deep-sky image registration and performs well compared to state-of-the-art descriptors.

关键词： image registration feature descriptor deep-skyimage rotation-invariant descriptor

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Two-step image registration by artificial immune system and chamfer matching

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Chinese Optics Letters 2008年第9期6卷 651-653页

作者：叶发茂徐少平熊宇虹 Department of Computer Science and Engineering Nanchang University

image registration is the precondition and foundation in the fusion of multi-source image data. A two-step approach based on artificial immune system and chamfer matching to register images from different types of sensors is presented. In the first step, it extracts the large edges and takes chamfer distance between the input image and the reference image as similarity measure and uses artificial immune network algorithm to speed up the searching of the initial transformation parameters. In the second step, an area-based method is utilized to refine the initial transformation and enhance the registration accuracy. Experimental results show that the proposed approach is a promising method for registration of multi-sensor images.

关键词： image enhancement image registration Immunology Mathematical transformations Network protocols Object recognition Sensor networks Sensors

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KW-SIFT descriptor for remote-sensing image registration

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Chinese Optics Letters 2011年第6期9卷 39-43页

作者：刘向增田铮延伟东段西发 School of Science Northwestern Polytechnical University

A technique to construct an affine invariant descriptor for remote-sensing image registration based on the scale invariant features transform （SIFT） in a kernel space is proposed. Affine invariant SIFT descriptor is first developed in an elliptical region determined by the Hessian matrix of the feature points. Thereafter, the descriptor is mapped to a feature space induced by a kernel, and a new descriptor is constructed by whitening the mapped descriptor in the feature space, with the transform called KW-SIFT. In a final step, the new descriptor is used to register remote-sensing images. Experimental results for remote-sensing image registration indicate that the proposed method improves the registration performance as compared with other related methods.

关键词： image registration

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