TY - GEN
T1 - Automated tumor segmentation using kernel sparse representations
AU - Thiagarajan, Jayaraman J.
AU - Rajan, Deepta
AU - Ramamurthy, Karthikeyan Natesan
AU - Frakes, David
AU - Spanias, Andreas
PY - 2012
Y1 - 2012
N2 - In this paper, we describe a pixel based approach for automated segmentation of tumor components from MR images. Sparse coding with data-adapted dictionaries has been successfully employed in several image recovery and vision problems. Since it is trivial to obtain sparse codes for pixel values, we propose to consider their non-linear similarities to perform kernel sparse coding in a high dimensional feature space. We develop the kernel K-lines clustering procedure for inferring kernel dictionaries and use the kernel sparse codes to determine if a pixel belongs to a tumorous region. By incorporating spatial locality information of the pixels, contiguous tumor regions can be efficiently identified. A low complexity segmentation approach, which allows the user to initialize the tumor region, is also presented. Results show that both of the proposed approaches lead to accurate tumor identification with a low false positive rate, when compared to manual segmentation by an expert.
AB - In this paper, we describe a pixel based approach for automated segmentation of tumor components from MR images. Sparse coding with data-adapted dictionaries has been successfully employed in several image recovery and vision problems. Since it is trivial to obtain sparse codes for pixel values, we propose to consider their non-linear similarities to perform kernel sparse coding in a high dimensional feature space. We develop the kernel K-lines clustering procedure for inferring kernel dictionaries and use the kernel sparse codes to determine if a pixel belongs to a tumorous region. By incorporating spatial locality information of the pixels, contiguous tumor regions can be efficiently identified. A low complexity segmentation approach, which allows the user to initialize the tumor region, is also presented. Results show that both of the proposed approaches lead to accurate tumor identification with a low false positive rate, when compared to manual segmentation by an expert.
KW - MRI
KW - kernel methods
KW - sparse representations
KW - tumor segmentation
UR - http://www.scopus.com/inward/record.url?scp=84872874128&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872874128&partnerID=8YFLogxK
U2 - 10.1109/BIBE.2012.6399658
DO - 10.1109/BIBE.2012.6399658
M3 - Conference contribution
AN - SCOPUS:84872874128
SN - 9781467343589
T3 - IEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012
SP - 401
EP - 406
BT - IEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012
T2 - 12th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2012
Y2 - 11 November 2012 through 13 November 2012
ER -