Medical Imaging and Information Sciences
Online ISSN : 1880-4977
Print ISSN : 0910-1543
ISSN-L : 0910-1543
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Volume 21, Issue 2
May
Displaying 1-4 of 4 articles from this issue
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Invited Lecture
Papers
  • Comparison of Automatic Setup Methods for Sub-Regions of Interest for Image Feature Analysis on Chest Radiographs
    Shinji SASAKI, Shoji KIDO, Hideyuki IWANAGA
    2004 Volume 21 Issue 2 Pages 187-193
    Published: 2004
    Released on J-STAGE: October 14, 2005
    DOIhttps://doi.org/10.11318/mii.21.187
    JOURNAL FREE ACCESS
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    To eliminate rib opacities on chest radiographs for quantitatively evaluation of lung opacities, we have developed an automatic algorithm, which sets up sub-regions of interest (sub-ROIs) for avoiding rib opacities in the analysis on chest radiographs. In the first step, a region of interest (ROI) was selected on a chest radiograph, and a 4th-order-polynomial surface was used for background density correction. In the next step, a one-directional Laplacian-Gaussian filter was used for enhancement of rib opacities. In the third step, the image was binarized on the ROI with a multiple threshold method, and then a morphological filter was used for elimination of noise components. Finally, we divided the ROI into some lattice regions and set up sub-ROIs. We calculated the area ratio of rib opacities in the sub-ROIs and judged the existence of rib opacities according to this sub-ROIs. We employed ROC analysis to evaluate the performance of this algorithm. In conclusion, this algorithm could set up sub-ROIs, which avoid rib opacities for image feature analysis of lung opacities.
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Letters
  • Development of an Automated Method for Differentiation of Cirrhotic Liver in Abdominal MR Images
    Wenguang LI, Xuejun ZHANG, Masayuki KANEMATSU, Takeshi HARA, Xiangrong ...
    2004 Volume 21 Issue 2 Pages 194-200
    Published: 2004
    Released on J-STAGE: October 14, 2005
    DOIhttps://doi.org/10.11318/mii.21.194
    JOURNAL FREE ACCESS
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    Cirrhosis of liver is a late stage of progressive liver disease defined as structural distortion of entire liver by fibrosis and parenchymal nodules. As the liver regenerates, fibrous connective tissue forms that may cause gross and microscopic distortion of normal hepatic morphology. In MR images, shape and texture analysis is regarded as an important and useful tool to differentiate cirrhosis from normal liver. In this paper, we propose a method to calculate the shape features from the segmented liver regions on MR image. Meanwhile, the texture features are quantified by using gray-level difference method (GLDM) within the small ROIs (regions of interest) selected in the liver region. The degree of liver cirrhosis is derived from integrating the shape and texture features of liver into a three-layer feed-forward artificial neural network (ANN). A liver is finally regarded as cirrhosis if the percentage of the ROIs with the degree over 0.5 is greater than 50%. The initial result showed that the ANN based method classified liver cirrhosis with a training accuracy of 100% on the 100 ROIs in the training set and that 82% liver cirrhosis and 100% normal cases were correctly differentiated from 18 test cases, which demonstrates the effectiveness of our proposed method.
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