3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationIEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012
Pages401-406
Number of pages6
DOIs
StatePublished - 2012
Event12th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2012 - Larnaca, Cyprus
Duration: Nov 11 2012Nov 13 2012

Publication series

NameIEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012

Other

Other12th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2012
Country/TerritoryCyprus
CityLarnaca
Period11/11/1211/13/12

Keywords

  • MRI
  • kernel methods
  • sparse representations
  • tumor segmentation

ASJC Scopus subject areas

  • Bioengineering
  • Biomedical Engineering

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