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Wang J, Xiang K, Chen K, Liu R, Ni R, Zhu H, Xiong Y. Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model. Front Neurosci 2022; 16:911957. [PMID: 35720703 PMCID: PMC9201218 DOI: 10.3389/fnins.2022.911957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 05/04/2022] [Indexed: 11/13/2022] Open
Abstract
In this paper, a method for medical image registration based on the bounded generalized Gaussian mixture model is proposed. The bounded generalized Gaussian mixture model is used to approach the joint intensity of source medical images. The mixture model is formulated based on a maximum likelihood framework, and is solved by an expectation-maximization algorithm. The registration performance of the proposed approach on different medical images is verified through extensive computer simulations. Empirical findings confirm that the proposed approach is significantly better than other conventional ones.
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Affiliation(s)
- Jingkun Wang
- Department of Orthopaedics, Daping Hospital, Army Medical University, Chongqing, China
| | - Kun Xiang
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Kuo Chen
- School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Rui Liu
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Ruifeng Ni
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Hao Zhu
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yan Xiong
- Department of Orthopaedics, Daping Hospital, Army Medical University, Chongqing, China
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Pichat J, Iglesias JE, Yousry T, Ourselin S, Modat M. A Survey of Methods for 3D Histology Reconstruction. Med Image Anal 2018; 46:73-105. [DOI: 10.1016/j.media.2018.02.004] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Revised: 02/02/2018] [Accepted: 02/14/2018] [Indexed: 02/08/2023]
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Ferrante E, Paragios N. Slice-to-volume medical image registration: A survey. Med Image Anal 2017; 39:101-123. [DOI: 10.1016/j.media.2017.04.010] [Citation(s) in RCA: 95] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Revised: 04/08/2017] [Accepted: 04/27/2017] [Indexed: 11/25/2022]
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Ohnishi T, Nakamura Y, Tanaka T, Tanaka T, Hashimoto N, Haneishi H, Batchelor TT, Gerstner ER, Taylor JW, Snuderl M, Yagi Y. Deformable image registration between pathological images and MR image via an optical macro image. Pathol Res Pract 2016; 212:927-936. [PMID: 27613662 PMCID: PMC5097673 DOI: 10.1016/j.prp.2016.07.018] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2015] [Revised: 07/02/2016] [Accepted: 07/31/2016] [Indexed: 02/05/2023]
Abstract
Computed tomography (CT) and magnetic resonance (MR) imaging have been widely used for visualizing the inside of the human body. However, in many cases, pathological diagnosis is conducted through a biopsy or resection of an organ to evaluate the condition of tissues as definitive diagnosis. To provide more advanced information onto CT or MR image, it is necessary to reveal the relationship between tissue information and image signals. We propose a registration scheme for a set of PT images of divided specimens and a 3D-MR image by reference to an optical macro image (OM image) captured by an optical camera. We conducted a fundamental study using a resected human brain after the death of a brain cancer patient. We constructed two kinds of registration processes using the OM image as the base for both registrations to make conversion parameters between the PT and MR images. The aligned PT images had shapes similar to the OM image. On the other hand, the extracted cross-sectional MR image was similar to the OM image. From these resultant conversion parameters, the corresponding region on the PT image could be searched and displayed when an arbitrary pixel on the MR image was selected. The relationship between the PT and MR images of the whole brain can be analyzed using the proposed method. We confirmed that same regions between the PT and MR images could be searched and displayed using resultant information obtained by the proposed method. In terms of the accuracy of proposed method, the TREs were 0.56±0.39mm and 0.87±0.42mm. We can analyze the relationship between tissue information and MR signals using the proposed method.
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Affiliation(s)
- Takashi Ohnishi
- Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan.
| | - Yuka Nakamura
- Graduate School of Engineering, Chiba University, Japan
| | - Toru Tanaka
- Graduate School of Engineering, Chiba University, Japan
| | - Takuya Tanaka
- Graduate School of Engineering, Chiba University, Japan
| | - Noriaki Hashimoto
- Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
| | - Hideaki Haneishi
- Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
| | - Tracy T Batchelor
- Massachusetts General Hospital Cancer Center, Boston, MA 02144, USA; Harvard Medical School, Boston, MA 02215, USA
| | - Elizabeth R Gerstner
- Massachusetts General Hospital Cancer Center, Boston, MA 02144, USA; Harvard Medical School, Boston, MA 02215, USA
| | - Jennie W Taylor
- Massachusetts General Hospital Cancer Center, Boston, MA 02144, USA; Harvard Medical School, Boston, MA 02215, USA
| | - Matija Snuderl
- New York University Langone Medical Center, New York, NY 10016, USA
| | - Yukako Yagi
- Harvard Medical School, Boston, MA 02215, USA; Massachusetts General Hospital Pathology Imaging and Communication Technology (PICT) Center, Boston, MA 02214, USA
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Xiao G, Bloch BN, Chappelow J, Genega EM, Rofsky NM, Lenkinski RE, Tomaszewski J, Feldman MD, Rosen M, Madabhushi A. Determining histology-MRI slice correspondences for defining MRI-based disease signatures of prostate cancer. Comput Med Imaging Graph 2011; 35:568-78. [PMID: 21255974 DOI: 10.1016/j.compmedimag.2010.12.003] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2010] [Revised: 12/10/2010] [Accepted: 12/13/2010] [Indexed: 11/30/2022]
Abstract
Mapping the spatial disease extent in a certain anatomical organ/tissue from histology images to radiological images is important in defining the disease signature in the radiological images. One such scenario is in the context of men with prostate cancer who have had pre-operative magnetic resonance imaging (MRI) before radical prostatectomy. For these cases, the prostate cancer extent from ex vivo whole-mount histology is to be mapped to in vivo MRI. The need for determining radiology-image-based disease signatures is important for (a) training radiologist residents and (b) for constructing an MRI-based computer aided diagnosis (CAD) system for disease detection in vivo. However, a prerequisite for this data mapping is the determination of slice correspondences (i.e. indices of each pair of corresponding image slices) between histological and magnetic resonance images. The explicit determination of such slice correspondences is especially indispensable when an accurate 3D reconstruction of the histological volume cannot be achieved because of (a) the limited tissue slices with unknown inter-slice spacing, and (b) obvious histological image artifacts (tissue loss or distortion). In the clinic practice, the histology-MRI slice correspondences are often determined visually by experienced radiologists and pathologists working in unison, but this procedure is laborious and time-consuming. We present an iterative method to automatically determine slice correspondence between images from histology and MRI via a group-wise comparison scheme, followed by 2D and 3D registration. The image slice correspondences obtained using our method were compared with the ground truth correspondences determined via consensus of multiple experts over a total of 23 patient studies. In most instances, the results of our method were very close to the results obtained via visual inspection by these experts.
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Affiliation(s)
- Gaoyu Xiao
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
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Hsu WY. Analytic differential approach for robust registration of rat brain histological images. Microsc Res Tech 2010; 74:523-30. [PMID: 20945464 DOI: 10.1002/jemt.20942] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2010] [Accepted: 08/27/2010] [Indexed: 11/08/2022]
Affiliation(s)
- Wei-Yen Hsu
- Institute of Statistical Science, Academia Sinica, Nankang, Taipei 115, Taiwan.
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Bertrand L, Nissanov J. The Neuroterrain 3D Mouse Brain Atlas. Front Neuroinform 2008; 2:3. [PMID: 18974795 PMCID: PMC2525976 DOI: 10.3389/neuro.11.003.2008] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2008] [Accepted: 07/10/2008] [Indexed: 11/13/2022] Open
Abstract
A significant objective of neuroinformatics is the construction of tools to readily access, search, and analyze anatomical imagery. This goal can be subdivided into development of the necessary databases and of the computer vision tools for image analysis. When considering mesoscale images, the latter tools can be further divided into registration algorithms and anatomical models. The models are atlases that contain both bitmap images and templates of anatomical boundaries. We report here on construction of such a model for the C57BL/6J mouse. The intended purpose of this atlas is to aid in automated delineation of the Mouse Brain Library, a database of brain histological images of importance to neurogenetic research.
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Affiliation(s)
- Louise Bertrand
- Department of Neurobiology and Anatomy, Drexel University College of MedicinePhiladelphia, PA, USA
| | - Jonathan Nissanov
- Department of Neurobiology and Anatomy, Drexel University College of MedicinePhiladelphia, PA, USA
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