A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
International Journal of Computer Vision
An Unbiased Detector of Curvilinear Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Voronoi-Based segmentation of cells on image manifolds
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
High-throughput analysis of multispectral images of breast cancer tissue
IEEE Transactions on Image Processing
Joint thrombus and vessel segmentation using dynamic texture likelihoods and shape prior
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
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Phase-contrast microscopy is a common approach for studying the dynamics of cell behaviors, such as cell migration. Cell segmentation is the basis of quantitative analysis of the immense cellular images. However, the complicated cell morphological appearance in phase-contrast microscopy images challenges the existing segmentation methods. This paper proposes a new cell segmentation method for cancer cell migration studies using phase-contrast images. Instead of segmenting cells directly based on commonly used low-level features, e.g. intensity and gradient, we first identify the leading protrusions, a high level feature, of cancer cells. Based on the identified cell leading protrusions, we introduce a front vector flow guided active contour, which guides the initial cell boundaries to the real boundaries. The experimental validation on a set of breast cancer cell images shows that the proposed method demonstrates fast, stable, and accurate segmentation for breast cancer cells with wide range of sizes and shapes.