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Proceedings Paper

Automated probabilistic segmentation of tumors from CT data using spatial and intensity properties
Author(s): Jung Leng Foo; Thom Lobe; Eliot Winer
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Paper Abstract

This paper presents a probabilistic segmentation process developed using the selection process from the Simulated Annealing optimization algorithm as a foundation. This process allows pixels to be segmented based on a probability selection process. An automated seed and search region selection processes multiple image slices automatically as an object's size, shape, and location changes between subsequent slices. Apart from the first slice in the dataset, where the user manually selects the seed and search region for segmentation, the method performs automatically for all other slices. From the test cases, the automated seed selection process was efficient in searching for new seed locations, as the object changed size, location, and orientation in each slice of the study. Segmentation results from both algorithms showed success in segmenting the tumor from nine of the ten CT datasets with less than 17% false positive errors and seven test cases with less than 20% false negative errors. Statistical testing of the results showed a high repeatability factor, with low values of inter- and intra-user variability. Furthermore, the method requires information from only a two-dimensional image data at a time to accommodate performance on a regular personal computer.

Paper Details

Date Published: 27 March 2009
PDF: 10 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725945 (27 March 2009); doi: 10.1117/12.811712
Show Author Affiliations
Jung Leng Foo, Iowa State Univ. (United States)
Thom Lobe, Blank Children's Hospital (United States)
Eliot Winer, Iowa State Univ. (United States)

Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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