Neural stem cell segmentation using local complex phase information

Taoyi Chen, Yong Zhang, Changhong Wang, Zhenshen Qu, Stephen T. Wong

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

2 Scopus citations

Abstract

Segmentation of neural stem cells is the preliminary step to treat and cure several brain neural diseases. There exist a number of methods to accomplish this task. However, all of these methods suffer from some problems, such as high intensity variation sensitivity, human interaction and high computational complexity. In this paper we proposed a novel edge-detection-based neural stem cell image segmentation algorithm using the local complex phase characteristics. The proposed method is an illumination and contrast invariant measurement of edge significance. Our contributions are that, local weighting summation Gaussian kernel convolution and a new model for phase deviation weighting function are introduced into the proposed model to improve the local phase measurement. In experiments, we show that the proposed method is more accurate and reliable than three existing gradient-based edge detection algorithms and Kovesi's model for neural stem cell image segmentation.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages3637-3640
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: Sep 26 2010Sep 29 2010

Other

Other2010 17th IEEE International Conference on Image Processing, ICIP 2010
CountryHong Kong
CityHong Kong
Period9/26/109/29/10

Keywords

  • Contrast invariant
  • Gradient-based
  • Image segmentation
  • Local complex phase
  • Neural stem cell

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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