Cell migration analysis using a statistical level-set segmentation on a wavelet-based structure tensor feature space

Asaad F. Said, Lina Karam

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

Abstract

In this paper, a new noise-resilient cell migration analysis scheme for bladder cancer cells is presented. The proposed scheme is based on texture segmentation in the wavelet domain using structure tensor features and adaptive statistical level-set segmentation. The proposed method extracts the region of interest where the cells are clustering, at different time instances, and computes the overall migration cell rate. For this purpose, the structure tensor data is processed using a trous wavelet filtering, which speeds up the algorithm as compared to existing nonlinear diffusion filters with the same accuracy. The proposed scheme is robust to noise and natural artifacts in the bladder cancer cell images. Moreover, the scheme can be applied successfully to images with poor contrast and high cell concentrations, even when the cells are overlapping and tiny. Simulation results are presented to show the performance of the proposed scheme.

Original languageEnglish (US)
Title of host publicationISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology
Pages473-478
Number of pages6
DOIs
StatePublished - Dec 1 2007
EventISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology - Cairo, Egypt
Duration: Dec 15 2007Dec 18 2007

Publication series

NameISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology

Other

OtherISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology
CountryEgypt
CityCairo
Period12/15/0712/18/07

Keywords

  • Bayesian formulation
  • Cell migration analysis
  • Level-set
  • PDE
  • Structure tensor
  • Texture segmentation
  • À trous wavelet

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Signal Processing

Fingerprint Dive into the research topics of 'Cell migration analysis using a statistical level-set segmentation on a wavelet-based structure tensor feature space'. Together they form a unique fingerprint.

  • Cite this

    Said, A. F., & Karam, L. (2007). Cell migration analysis using a statistical level-set segmentation on a wavelet-based structure tensor feature space. In ISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology (pp. 473-478). [4458183] (ISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology). https://doi.org/10.1109/ISSPIT.2007.4458183