Enhanced level-set approach to segmentation of 3-D heterogeneous lesions from dynamic contrast-enhanced MR images

Nikhil S. Rajguru, Jeffrey J. Rodríguez, Natarajan Raghunand, Robert J. Gillies

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

1 Scopus citations

Abstract

A method for the 3-D segmentation of a heterogeneous (non-uniform intensity) volume of interest from high-resolution dynamic contrast-enhanced magnetic resonance image acquisition is presented and evaluated in this paper. The algorithm uses a level-set approach with enhanced edge sensitivity. The local probability near the lesion boundary is utilized for level-set evolution. The level-set function is sensitized to the boundary of the lesion by combining forces derived from the image, including gradient vector flow. The proposed algorithm performs better than other applicable segmentation techniques based on active contours, and also the classical level-set approach. Results are presented and analyzed to validate the same.

Original languageEnglish (US)
Title of host publication7th IEEE Southwest Symposium on Image Analysis and Interpretation
Pages71-75
Number of pages5
StatePublished - Nov 21 2006
Event7th IEEE Southwest Symposium on Image Analysis and Interpretation - Denver, CO, United States
Duration: Mar 26 2006Mar 28 2006

Publication series

NameProceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation
Volume2006

Other

Other7th IEEE Southwest Symposium on Image Analysis and Interpretation
CountryUnited States
CityDenver, CO
Period3/26/063/28/06

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ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this

Rajguru, N. S., Rodríguez, J. J., Raghunand, N., & Gillies, R. J. (2006). Enhanced level-set approach to segmentation of 3-D heterogeneous lesions from dynamic contrast-enhanced MR images. In 7th IEEE Southwest Symposium on Image Analysis and Interpretation (pp. 71-75). [1633724] (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation; Vol. 2006).