Complex local phase based subjective surfaces (CLAPSS) and its application to DIC red blood cell image segmentation

  • Authors:

  • Taoyi Chen;Yong Zhang;Changhong Wang;Zhenshen Qu;Fei Wang;Tanveer Syeda-Mahmood

  • Affiliations:

  • The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, Hebei 050081, China and Department of Control Science and Engineering, Harbin Institute of Technology, ...;Healthcare Informatics, IBM Almaden Research Center, San Jose, CA 95120, USA;Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150080, China;Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150080, China;Healthcare Informatics, IBM Almaden Research Center, San Jose, CA 95120, USA;Healthcare Informatics, IBM Almaden Research Center, San Jose, CA 95120, USA

  • Venue:
  • Neurocomputing
  • Year:
  • 2013

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Abstract

Differential Interference Contrast (DIC) microscopy is a common approach for researching the dynamics of cell behaviors. Segmentation of shape of erythrocyte (red blood cell) is the basis of quantitative analysis of its deformability and hence its filterability. Commonly used manual segmentation of shapes of individual cells from samples by human visual inspection requires a large amount of tedious work because it is time consuming and exhaustive. This makes automatic cell image analysis essential in biology studies. In this paper, a novel level set based technique, called Complex Local Phase based Subjective Surfaces (CLAPSS), is proposed for the segmentation of differential interference contrast (DIC) red blood cell microscopy images. Based on the framework of a generalized version of subjective surfaces (GSUBSURF), a complex local phase based edge indicator function is introduced to replace the traditional gradient based edge detector for the local image feature acquisition, which is the key for the evolution of the surface. In addition, we propose a new variation scheme for stretching factor to achieve relatively accurate segmentation results even if the reference point is located nearby cell boundaries. We show that the proposed method is more accurate and reliable than several existing methods in experiments.