Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8041
Title: Object localization in medical images based on graphical model with contrast and interest-region terms
Authors: Song, Y
Cai, W
Huang, H
Wang, Y
Feng, D
Keywords: Computerised tomography
Graph theory
Image classification
Image coding
Image segmentation
Medical image processing
Positron emission tomography
Issue Date: 2012
Publisher: IEEE
Source: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 16-21 June 2012, Providence, RI, p. 1-7 How to cite?
Abstract: In this paper, we propose a novel method for object localization, generally applicable to medical images in which the objects can be distinguished from the background mainly based on feature differences. We design a new CRF model with additional contrast and interest-region potentials, which encode the higher-order contextual information between regions, on the global and structural levels. We also propose a sparse-coding based classification approach for the interest-region detection with discriminative dictionaries, to serve as a second opinion for more accurate region labeling. We evaluate our object localization method on two medical imaging applications: lesion dissimilarity on thoracic PET-CT images, and cell segmentation on microscopic images. Our evaluations show higher performance when comparing to recently reported approaches.
URI: http://hdl.handle.net/10397/8041
ISBN: 978-1-4673-1611-8
978-1-4673-1610-1 (E-ISBN)
ISSN: 2160-7508
DOI: 10.1109/CVPRW.2012.6239240
Appears in Collections:Conference Paper

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