title: Multi-classification of cell deformation based on object alignment and run length statistic.
abstract: Cellular morphology is widely applied in digital pathology and is essential for improving our understanding of the basic physiological processes of organisms. One of the main issues of application is to develop efficient methods for cell deformation measurement. We propose an innovative indirect approach to analyze dynamic cell morphology in image sequences. The proposed approach considers both the cellular shape change and cytoplasm variation, and takes each frame in the image sequence into account. The cell deformation is measured by the minimum energy function of object alignment, which is invariant to object pose. Then an indirect analysis strategy is employed to overcome the limitation of gradual deformation by run length statistic. We demonstrate the power of the proposed approach with one application: multi-classification of cell deformation. Experimental results show that the proposed method is sensitive to the morphology variation and performs better than standard shape representation methods.
authors.forename: Heng
authors.initials: H
authors.lastname: Li
authors.forename: Zhiwen
authors.initials: Z
authors.lastname: Liu
authors.forename: Xing
authors.initials: X
authors.lastname: An
authors.forename: Yonggang
authors.initials: Y
authors.lastname: Shi
mesh_terms: D000465:Algorithms; D000818:Animals; D048430:Cell Shape; D007091:Image Processing, Computer-Assisted; D008297:Male; D008810:Mice, Inbred C57BL