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Proceedings Paper

A classification method of liver tumors based on temporal change of Hounsfield unit in CT images
Author(s): Masaki Ishiguro; Ichiro Murase; Noriyuki Moriyama; Ryuzo Sekiguchi
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Paper Abstract

We present an automatic diagnosis method of liver cancer by using sequential images with contrast material of dynamic CT. Our method identifies and classifies liver tumors by extracting temporal change of CT values [Hounsfield Unit(HU)] of tumors from four kinds of CT images (i.e. plain CT, early phase, portal phase, late phase of dynamic CT images) in addition to morphological features of tumors. Automatic diagnosis of liver tumors is very difficult, because contrast of liver tumors is very small compared with liver background, shapes of tumors are diverse, and extraction of temporal change of CT values is very difficult due to morphological and contrast complexity of temporal change of tumor segments. Our method extracts temporal change of CT values of objects by mapping segments of same objects in different CT phase based on overlap ratio and position adjustment. We also implemented a graphical user interface for searching such images from an image database that include tumors similar to an image given as a search condition with respect to features of morphorogical and temporal change of contrast.

Paper Details

Date Published: 29 April 2005
PDF: 9 pages
Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); doi: 10.1117/12.590245
Show Author Affiliations
Masaki Ishiguro, Mitsubishi Research Institute, Inc. (Japan)
Ichiro Murase, Mitsubishi Research Institute, Inc. (Japan)
Noriyuki Moriyama, National Cancer Ctr. (Japan)
Ryuzo Sekiguchi, National Cancer Ctr. Hospital East (Japan)

Published in SPIE Proceedings Vol. 5747:
Medical Imaging 2005: Image Processing
J. Michael Fitzpatrick; Joseph M. Reinhardt, Editor(s)

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