Object Localization in Medical Images Based on Graphical Model with Contrast and Interest-Region Terms
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.
Cite
Text
Song et al. "Object Localization in Medical Images Based on Graphical Model with Contrast and Interest-Region Terms." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2012. doi:10.1109/CVPRW.2012.6239240Markdown
[Song et al. "Object Localization in Medical Images Based on Graphical Model with Contrast and Interest-Region Terms." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2012.](https://mlanthology.org/cvprw/2012/song2012cvprw-object/) doi:10.1109/CVPRW.2012.6239240BibTeX
@inproceedings{song2012cvprw-object,
title = {{Object Localization in Medical Images Based on Graphical Model with Contrast and Interest-Region Terms}},
author = {Song, Yang and Cai, Weidong and Huang, Heng and Wang, Yue and Feng, David Dagan},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2012},
pages = {1-7},
doi = {10.1109/CVPRW.2012.6239240},
url = {https://mlanthology.org/cvprw/2012/song2012cvprw-object/}
}