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Local complex phase based level set and its application to DIC red blood cell segmentation

  • Taoyi Chen*
  • , Yong Zhang
  • , Changhong Wang
  • , Zhenshen Qu
  • , Maomao Cai
  • , Fei Wang
  • , Tanveer Syeda-Mahmood
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Cell segmentation in microscopy imagery is essential for many biomedical applications. In this paper, a novel level set based technique is proposed for segmenting the differential interference contrast (DIC) red blood cell microscopy images. Based on the framework of subjective surfaces, a local complex phase based edge indicator function is introduced to replace the traditional gradient-based edge detection method for the local image feature acquisition, which is the key for the evolution of the surface. In addition, to help improve the detection of cell edges, we propose a modified version of level set framework in subjective surfaces embedded with shape prior information. In experiments, we show that the proposed method is more accurate and reliable than several existing typical level set methods for DIC red blood cell image segmentation.

Original languageEnglish
Title of host publication2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI'11
PublisherIEEE Computer Society
Pages187-190
Number of pages4
ISBN (Print)9781424441280
DOIs
StatePublished - 2011
Event8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011 - Chicago, IL, United States
Duration: 30 Mar 20112 Apr 2011

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011
Country/TerritoryUnited States
CityChicago, IL
Period30/03/112/04/11

Keywords

  • Cell segmentation
  • local complex phase
  • red blood cell
  • shape prior
  • subjective surfaces

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