Segmentation of drosophila RNAi fluorescence images using level sets

Guanglei Xiong, Xiaobo Zhou, Liang Ji, Pamela Bradley, Norbert Perrimon, Stephen Wong

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

25 Scopus citations

Abstract

Image-based, high throughput genome-wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Robust automated segmentation of the large volumes of output images generated from image-based screening is much needed for data analyses. In this paper, we propose a new automated segmentation technique to fill the void. The technique consists of two steps: nuclei and cytoplasm segmentation. In the former step, nuclei are extracted, labeled and used as starting points for the latter. A new force obtained from rough segmentation is introduced into the classical level set curve evolution to improve the performance for odd shapes, such as spiky or ruffly cells. A scheme of preventing curves from crossing is proposed to treat the difficulty of segmenting touching cells. We apply it to three types of drosophila cells in RNAi fluorescence images. In all cases, greater than 92% accuracy is obtained.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages73-76
Number of pages4
DOIs
StatePublished - 2006
Event2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States
Duration: Oct 8 2006Oct 11 2006

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2006 IEEE International Conference on Image Processing, ICIP 2006
CountryUnited States
CityAtlanta, GA
Period10/8/0610/11/06

Keywords

  • Deformable model
  • Geodesic active contour
  • Geometric active contour
  • Image segmentation
  • Level sets

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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