Liver segmentation using structured sparse representations

Vimal Singh, Dan Wang, Ahmed H. Tewfik, Bradley J. Erickson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Segmentation of liver from volumetric images forms the basis for surgical planning required for living donor transplantations and tumor resections surgeries. This paper introduces a novel idea of using sparse representations of liver shapes in a learned structured dictionary to produce an accurate preliminary segmentation, which is further evolved using a joint image and shape based level-set framework to obtain the final segmented volume. Structured dictionary for liver shapes can be learned from an available training dataset. The proposed approach requires only 3 orthogonal segmented masks as user-input, which is less than half the number required by current state-of-the-art interaction-based methods. The increased accuracy of the preliminary segmentation translates into faster convergence of the evolution step and highly accurate final segmentations with mean average symmetric surface distances (ASSD) [1] of (1.03±0.3)mm when tested on a challenging dataset containing 62 volumes. Our approach segments a volume on an average of 5 mins and, is 25% (approx.) faster than comparably performing techniques.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages565-568
Number of pages4
DOIs
StatePublished - Oct 23 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: Mar 25 2012Mar 30 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
CountryJapan
CityKyoto
Period3/25/123/30/12

Keywords

  • Level-set Evolution
  • Semi-Automatic Segmentation
  • Sparse Representations
  • Structured Sparsity
  • Subspace Clustering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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    Singh, V., Wang, D., Tewfik, A. H., & Erickson, B. J. (2012). Liver segmentation using structured sparse representations. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings (pp. 565-568). [6287942] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2012.6287942