title: Segmentation and Tracking of Lymphocytes Based on Modified Active Contour Models in Phase Contrast Microscopy Images.
abstract: The paper proposes an improved active contour model for segmenting and tracking accurate boundaries of the single lymphocyte in phase-contrast microscopic images. Active contour models have been widely used in object segmentation and tracking. However, current external-force-inspired methods are weak at handling low-contrast edges and suffer from initialization sensitivity. In order to segment low-contrast boundaries, we combine the region information of the object, extracted by morphology gray-scale reconstruction, and the edge information, extracted by the Laplacian of Gaussian filter, to obtain an improved feature map to compute the external force field for the evolution of active contours. To alleviate initial location sensitivity, we set the initial contour close to the real boundaries by performing morphological image processing. The proposed method was tested on live lymphocyte images acquired through the phase-contrast microscope from the blood samples of mice, and comparative experimental results showed the advantages of the proposed method in terms of the accuracy and the speed. Tracking experiments showed that the proposed method can accurately segment and track lymphocyte boundaries in microscopic images over time even in the presence of low-contrast edges, which will provide a good prerequisite for the quantitative analysis of lymphocyte morphology and motility.
authors.affiliation: Institute of Signal and Image Processing, School of Information and Electronics, Beijing Institute of Technology (BIT), 5 South Zhongguancun Street, Haidian District, Beijing 100081, China ; College of Electronics and Information Engineering, Hebei University, Baoding, China ; Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, China.
authors.forename: Yali
authors.identifier: 0000-0001-8263-6368
authors.initials: Y
authors.lastname: Huang
authors.affiliation: Institute of Signal and Image Processing, School of Information and Electronics, Beijing Institute of Technology (BIT), 5 South Zhongguancun Street, Haidian District, Beijing 100081, China.
authors.forename: Zhiwen
authors.initials: Z
authors.lastname: Liu
mesh_terms: D000818:Animals; D002465:Cell Movement; D048430:Cell Shape; D058948:Cell Tracking / Q000379:methods* / Q000706:statistics & numerical data; D019295:Computational Biology; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data; D008214:Lymphocytes / Q000166:cytology* / Q000502:physiology*; D051379:Mice; D008858:Microscopy, Phase-Contrast / Q000706:statistics & numerical data*