An affine transformation invariance approach to cell tracking.
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 microm, 0.79 microm and 0.74 microm, 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 by using the number of frames tracked as an evaluation measure. Dark target leukocyte possesses similar image intensity to the background, making it difficult to be located. In 20 sequences where the target leukocyte was dark, the affine transformation invariance approach tracked more frames in 18 sequences and fewer frames in 2 sequences when compared with the active contour tracking method. In comparison with the Monte Carlo tracker, the affine invariance method tracked more frames in 9 sequences, the same number of frames in 7 sequences and fewer frames in 4 sequences. In tracking a bright target leukocyte in 30 sequences, the affine transformation invariance approach demonstrated superior performance in 7 sequences and inferior performance in 1 sequence when compared with the active contour tracking method. It outperformed the Monte Carlo tracker in 15 sequences and underperformed in 1 sequence.
Cui
Jing
J
Department of Radiology, University of Michigan, Ann Arbor, MI 48109-5842, United States. jingcui@med.umich.edu
Ray
Nilanjan
N
Acton
Scott T
ST
Lin
Zongli
Z
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002465:Cell Movement / Q000502:physiology; D002478:Cells, Cultured; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007962:Leukocytes / Q000166:cytology* / Q000502:physiology*; D018715:Microscopy, Video / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique