Medical Imaging Technology
Online ISSN : 2185-3193
Print ISSN : 0288-450X
ISSN-L : 0288-450X
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Volume 33, Issue 3
Displaying 1-15 of 15 articles from this issue
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Main Topics / Bioimaging
  • Bioimaging: Introduction
    Hideo YOKOTA
    2015 Volume 33 Issue 3 Pages 75-76
    Published: 2015
    Released on J-STAGE: May 26, 2015
    DOIhttps://doi.org/10.11409/mit.33.75
    JOURNAL FREE ACCESS
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  • Live Cell 4-Dimensional Imaging
    Akihiko NAKANO
    2015 Volume 33 Issue 3 Pages 77-83
    Published: 2015
    Released on J-STAGE: May 26, 2015
    DOIhttps://doi.org/10.11409/mit.33.77
    JOURNAL FREE ACCESS
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    Super-resolution microscopies are emerging. Not only spatial resolution but also temporal resolution is much improving and it is now possible to image molecular behaviors within live cells in extreme 4 dimensions. Some examples of application to the cutting-edge issues of cell biology will be described.
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  • Bioimage Analysis for Quantitative Biology and Its Application to Mammalian Embryogenesis
    Tetsuya J. KOBAYASHI, Md. Khayrul BASHAR, Akira FUNAHASHI, Toshihiko F ...
    2015 Volume 33 Issue 3 Pages 84-89
    Published: 2015
    Released on J-STAGE: May 26, 2015
    DOIhttps://doi.org/10.11409/mit.33.84
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    Driven by the rapid development of bioimaging, image analysis is now attracting much attention as the technology to extract quantitative information from bioimaging data. In this article, we briefly review recent applications of image analysis to bioimaging data, and introduce our ongoing research on the reconstruction of developmental dynamics of mammalian preimplantation embryos from bioimaging data.
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  • Particle Detection and Tracking in Intracelluar Images
    Kazuhiro HOTTA
    2015 Volume 33 Issue 3 Pages 90-96
    Published: 2015
    Released on J-STAGE: May 26, 2015
    DOIhttps://doi.org/10.11409/mit.33.90
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    We describe the particle detection and tracking methods in intracellular images. Two kinds of particle detection methods are introduced when we have many and less supervised data. When we have small number of supervised data, the detection accuracy is improved by using contextual information of a particle. In particle tracking, we use posterior probability of SIFT features. We improve the tracking accuracy by considering the possibility of mis-tracking.
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  • Optimization Techniques for Bioimage-informatics
    Seiichi UCHIDA
    2015 Volume 33 Issue 3 Pages 97-104
    Published: 2015
    Released on J-STAGE: May 26, 2015
    DOIhttps://doi.org/10.11409/mit.33.97
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    Bioimage-informatics is a new collaborative research area for biologist and image-informatics researchers. Tasks of bioimage-informatics are often very difficult because bioimages capture unclear, ambiguous and multiple targets under various noise and low time-space resolution. This difficulty suggests the necessity of robust image processing techniques. One promising strategy for developing such techniques is to utilize various optimization methods. Optimization methods aim to have the best solution among all possible solutions and thus will provide better results than naive and heuristic methods. On the other hands, thoughtless formulation of an image processing task as an optimization problem results in prohibitive computational complexity and it is impossible to have the optimal solution of the problem. We, therefore, need to formulate the problem as practically solvable one by a certain optimization method. This article outline several examples of optimization problems and their application to bioimage-informatics tasks.
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  • Automatic Classification of Images for Bioimaging Analysis
    Natsumaro KUTSUNA
    2015 Volume 33 Issue 3 Pages 105-111
    Published: 2015
    Released on J-STAGE: May 26, 2015
    DOIhttps://doi.org/10.11409/mit.33.105
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    Advances in imaging techniques have yielded massive images into the biology. Along with the increase of dimension and data size of bioimages in the research field, a need for computer-aided image analysis becomes clear. However, software environments are not utilized enough for image analysis. This is because the versatility of purposes and the diversity of bioimages. In this review, approaches for development of bioimage classifiers are outlined as follows: unsupervised learning, supervised learning and active learning. We have developed an adaptive classification system for bioimages, named "clustering-aided rapid training agent (CARTA)". The CARTA is applicable to various bioimage classification that facilitates annotation and selection of features. The CARTA interactively collects information from experts and generates the customized classifier for the specified bioimages.
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  • Cloud-based Systems for Biomedical Image Processing
    Masahiko MORITA, Shin YOSHIZAWA, Takashi IJIRI, Takehiro TAWARA, Masao ...
    2015 Volume 33 Issue 3 Pages 112-117
    Published: 2015
    Released on J-STAGE: May 26, 2015
    DOIhttps://doi.org/10.11409/mit.33.112
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    Modern bio-medical sciences require image processing tools for collaborative research and development (R&D) because of recent advances in image acquisition technology, such as CT, MRI and laser microscopies. We present a brief review on biomedical image processing systems in terms of collaborative R&D through network environments.
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Papers
  • Fast B1 Mapping Method Eliminating the Effect of T1 Relaxation
    Kosuke ITO, Masahiro TAKIZAWA, Tetsuhiko TAKAHASHI
    2015 Volume 33 Issue 3 Pages 118-123
    Published: 2015
    Released on J-STAGE: May 26, 2015
    DOIhttps://doi.org/10.11409/mit.33.118
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    In high field MRI systems, the wavelength of transmitted RF pulse is same order as human body size. So the phase of transmitted RF pulse changes in human body, spatial distribution of RF pulse (B1 map) becomes inhomogeneous. Inhomogeneous B1 map causes some clinical problems like shading, SNR loss, and so on. RF shimming using multi channel RF transmit coil is one of methods to resolve this problem. B1 map for each RF transmit channel is used in RF shimming. To measure B1 maps correctly, the effect of T1 relaxation should be eliminated appropriately, thus long repetition time of pulse sequence is used and the scan time becomes long in previous methods. We developed a new fast B1 mapping method using pre-pulse (multi Td method). In multi Td method, B1 maps are calculated by using images acquired before and after pre-pulse is applied. Acquiring all images sequentially, scan time is 5.5 sec for 2-channel RF transmit coil. Also, using images acquired before pre-pulse is applied, the effect of T1 relaxation is eliminated and accurate B1 maps are calculated.
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  • Development of Fully Automatic Image Analysis System for Quantitation of Ki-67 Staining in Breast Cancer
    Yoshiko YAMASHITA, Tomoharu KIYUNA, Masahiro YAMAGUCHI
    2015 Volume 33 Issue 3 Pages 124-132
    Published: 2015
    Released on J-STAGE: May 26, 2015
    DOIhttps://doi.org/10.11409/mit.33.124
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    Ki-67 index is widely used for benign-malignant discrimination, diagnosis of the malignancy, and prognosis. We propose and develop a nuclear analysis system for the immunohistochemical staining image. In our method of Whole Slide Image (WSI) analysis, the region for the analysis is automatically selected and no individual conditionalization for each image pattern is necessary. There is a high correlation between Ki-67 index computed by our system and that by a visual count, indicating validity of our method.
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Work-in-progress
  • Improvement of Kilo-voltage Cone Beam CT Image Quality with Iterative Optimized Scatter Correction Algorithm
    Satoshi KIDA, Yoshitaka MASUTANI, Masahiro NAKANO, Toshikazu IMAE, Kei ...
    2015 Volume 33 Issue 3 Pages 133-141
    Published: 2015
    Released on J-STAGE: May 26, 2015
    DOIhttps://doi.org/10.11409/mit.33.133
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    In this study, we developed the iterative optimized scatter correction algorithm, which incorporates scatter correction and statistical iterative reconstruction method for improvement of CBCT image quality. For scatter estimation, scatter measurement with beam blockers and scatter simulation based on Klein-Nishina formula were combined complementally. Statistical image reconstruction (Convex method) and scatter estimation were treated parallelly and incorporated into the iterative optimized scatter correction algorithm. The validation of our proposed method was performed by analysis of the attenuation coefficients of cylindrical water phantom.
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Tutorial
  • Fundamental and Frontier of 3D Modeling and Visualization, Its Application to Medical Image (2): Transparent and Fused Visualization of Medical Data by Using Particle-based Rendering
    Satoshi TANAKA, Kyoko HASEGAWA, Rui XU
    2015 Volume 33 Issue 3 Pages 142-146
    Published: 2015
    Released on J-STAGE: May 26, 2015
    DOIhttps://doi.org/10.11409/mit.33.142
    JOURNAL FREE ACCESS
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    For transparent visualization of medical volume data acquired by CT or MRI, realization of correct sense of depth is important. Therefore, the conventional visualization methods execute depth sorting of rendering primitives along the line of sight. However, the indefiniteness of depth-sorted orders of rendering primitives, which appears, for example, for overlapping boundaries of human organs and very fine 3D inner structures of a human body, may cause the rendering artifacts. Moreover, the depth sorting requires long computation time when treating large data. To avoid these difficulties, we proposed particle-based rendering that is a kind of stochastic point-based rendering. This rendering does not require the depth sorting and thus free from the above-mentioned difficulties. Moreover, it is easy to execute fused visualization of a medical volume, slice and surfaces only by expressing each of them with small particles. Such fused visualization helps us conceive 3D inner structures of a human body.
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Reports
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