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Available online at www.sciencedirect.com Computerized Medical Imaging and Graphics 32 (2008) 554565
 

Summary: Available online at www.sciencedirect.com
Computerized Medical Imaging and Graphics 32 (2008) 554565
An affine transformation invariance approach to cell tracking
Jing Cuia,, Nilanjan Rayb,1, Scott T. Actonc,2, Zongli Linc,3
a Department of Radiology, University of Michigan, Med-Inn C473, 1500 E. Medical Center Drive, Ann Arbor, MI 48109-5842, United States
b Department of Computing Science, 2-21 Athabasca Hall, University of Alberta, Edmonton, Alberta, Canada T6G 2E8
c Charles L. Brown Department of Electrical and Computer Engineering, Thornton Hall, University of Virginia,
351 McCormick Road, P.O. Box 400743, Charlottesville, VA 22904-4743, United States
Received 16 August 2007; received in revised form 17 June 2008; accepted 19 June 2008
Abstract
Accurate and robust methods for automatically tracking rolling leukocytes facilitate inflammation research as leukocyte motion is a primary
indicator of inflammatory response in the microvasculature. This paper reports on an affine transformation invariance approach we proposed to
track rolling leukocyte in intravital microscopy image sequences. The method is based on the affine transformation invariance property, which
enables the accommodation of linear affine transformations (translation, rotation, and/or scaling) of the target, and a particle filter that overcomes
the effect of image clutter. In our data set of 50 sequences, we compared the new approach with an active contour tracking method and a Monte
Carlo tracker. With the manual tracking result provided by an operator as the reference, the root mean square errors for the active contour tracking
method, the Monte Carlo tracker and the affine transformation invariance approach were 0.95 m, 0.79 m and 0.74 m, respectively, and the
percentage of frames tracked were 72%, 75% and 89%, respectively. The affine transformation invariance approach demonstrated more robust
(being able to successfully locate target leukocyte in more frames) and more accurate (lower root mean square error) tracking performance. We
also separately studied the ability of the affine transformation invariance approach to track a dark target leukocyte and a bright target leukocyte

  

Source: Acton, Scott - Department of Electrical and Computer Engineering, University of Virginia
Ray, Nilanjan - Department of Computing Science, University of Alberta

 

Collections: Computer Technologies and Information Sciences; Engineering