Tumor segmentation with multi-modality image in conditional random field framework with logistic regression models Conference Paper


Authors: Hu, Y. C.; Grossberg, M.; Mageras, G.
Title: Tumor segmentation with multi-modality image in conditional random field framework with logistic regression models
Conference Title: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Abstract: We have developed a semi-automatic method for multi-modality image segmentation aimed at reducing the manual process time via machine learning while preserving human guidance. Rather than reliance on heuristics, human oversight and expert training from images is incorporated into logistic regression models. The latter serve to estimate the probability of tissue class assignment for each voxel as well as the probability of tissue boundary occurring between neighboring voxels given the multi-modal image intensities. The regression models provide parameters for a Conditional Random Field (CRF) framework that defines an energy function with the regional and boundary probabilistic terms. Using this CRF, a max-flow/min-cut algorithm is used to segment other slices in the 3D image set automatically with options of addition user input. We apply this approach to segment visible tumors in multi-modal medical volumetric images.
Keywords: pet; lung-cancer; delineation; ct images; graph cuts; auto-segmentation
Journal Title IEEE Engineering in Medicine and Biology Society. Conference Proceedings
Conference Dates: 2014 Aug 26-30
Conference Location: Chicago, IL
ISBN: 1557-170X
Publisher: IEEE  
Date Published: 2014-01-01
Start Page: 6450
End Page: 6454
Language: English
ACCESSION: WOS:000350044706109
PROVIDER: wos
PUBMED: 25571473
DOI: 10.1109/EMBC.2014.6945105
Notes: Proceedings Paper -- 36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC) -- AUG 26-30, 2014 -- Chicago, IL -- 978-1-4244-7929-0 -- Source: Wos
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  1. Gikas S Mageras
    277 Mageras
  2. Yu-Chi Hu
    120 Hu