Cell Segmentation and Tracking Using Texture-Adaptive Snakes

Wang, Xiaoxu; He, Weijun; Metaxas, Dimitris N.; Mathew, Robin; White, Eileen

doi:10.1109/isbi.2007.356798

Cited by 45 publications

(26 citation statements)

References 7 publications

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“…Image segmentation of cells is an active research area of biomedical image processing, with active contour methods being considered the first choice of cell image segmentation. 15 Both the parametric form (i.e., snake mode) contour approaches, 16,17 as well as the nonparametric approach (i.e., level set), [18][19][20] have been successfully demonstrated in cell detection and tracking studies. Compared to the contour-based, the traditional threshold methods for segmentation tend to be more prone to noise, but are conceptually simpler and often very effective, 21 also suggested by previous yeast cell morphological studies.…”

Section: Image Processing Algorithmsmentioning

confidence: 99%

Image processing and classification algorithm for yeast cell morphology in a microfluidic chip

et al. 2011

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Abstract. The study of yeast cell morphology requires consistent identification of cell cycle phases based on cell bud size. A computer-based image processing algorithm is designed to automatically classify microscopic images of yeast cells in a microfluidic channel environment. The images were enhanced to reduce background noise, and a robust segmentation algorithm is developed to extract geometrical features including compactness, axis ratio, and bud size. The features are then used for classification, and the accuracy of various machine-learning classifiers is compared. The linear support vector machine, distance-based classification, and k-nearest-neighbor algorithm were the classifiers used in this experiment. The performance of the system under various illumination and focusing conditions were also tested. The results suggest it is possible to automatically classify yeast cells based on their morphological characteristics with noisy and low-contrast images. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

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Section: Image Processing Algorithmsmentioning

confidence: 99%

Image processing and classification algorithm for yeast cell morphology in a microfluidic chip

et al. 2011

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“…A vast literature exists on the use of snakes to segment biological structures such as biological tissues (nerve fibers [50], [51]), cell structures (mitochondria [52]), protein-based structures (actin filaments [53]), or model organisms such as zebra fish embryos [54] or C. elegans (see Figure 5). The versatile nature of snakes makes them suitable for problems that combine segmentation and tracking (Leukocyte tracking [55], motility analysis [56]), organelle tracking (microtubule tracking), or even the reconstruction of cell lineages [57]. …”

Section: Biomedical Applicationsmentioning

confidence: 99%

Snakes on a Plane: A perfect snap for bioimage analysis

Delgado-Gonzalo

Uhlmann

Schmitter

et al. 2015

IEEE Signal Process. Mag.

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“…Such methods include thresholding, the watershed method and texture analysis (Wu et al, 1995;Koyuncu et al, 2012;Korzynska et al, 2007). Alternatively, active contour algorithms that capture cell boundaries have been used (Tscherepanow et al, 2008;Wang, He & Metaxas, 2007;Ali et al, 2007, Seroussi et al, 2012. The shortcomings in commonly used techniques is the requirement for image preprocessing or the necessity to use accompanying techniques due to nonuniformities of pixel intensities inside the segmented cells, similarities in the background and specimen pixel intensities, or because the cell boundaries cannot be clearly resolved (Wu et al, 1995;Tse et al, 2009;Debeir et al, 2005;Ali et al, 2007;Yin et al, 2012;Ambühl et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Automatic detection and analysis of cell motility in phase‐contrast time‐lapse images using a combination of maximally stable extremal regions and Kalman filter approaches

Kaakinen

Huttunen

Paavolainen

et al. 2013

Journal of Microscopy

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SummaryPhase-contrast illumination is simple and most commonly used microscopic method to observe nonstained living cells. Automatic cell segmentation and motion analysis provide tools to analyze single cell motility in large cell populations. However, the challenge is to find a sophisticated method that is sufficiently accurate to generate reliable results, robust to function under the wide range of illumination conditions encountered in phase-contrast microscopy, and also computationally light for efficient analysis of large number of cells and image frames. To develop better automatic tools for analysis of low magnification phase-contrast images in time-lapse cell migration movies, we investigated the performance of cell segmentation method that is based on the intrinsic properties of maximally stable extremal regions (MSER). MSER was found to be reliable and effective in a wide range of experimental conditions. When compared to the commonly used segmentation approaches, MSER required negligible preoptimization steps thus dramatically reducing the computation time. To analyze cell migration characteristics in time-lapse movies, the MSER-based automatic cell detection was accompanied by a Kalman filter multiobject tracker that efficiently tracked individual cells even in confluent cell populations. This allowed quantitative cell motion analysis resulting in accurate measurements of the migration magnitude and direction of individual cells, as well as characteristics of collective migration fax: +358 8 537 6115; e-mail: lauri.eklund@oulu.fi of cell groups. Our results demonstrate that MSER accompanied by temporal data association is a powerful tool for accurate and reliable analysis of the dynamic behaviour of cells in phase-contrast image sequences. These techniques tolerate varying and nonoptimal imaging conditions and due to their relatively light computational requirements they should help to resolve problems in computationally demanding and often time-consuming large-scale dynamical analysis of cultured cells.

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Cell Segmentation and Tracking Using Texture-Adaptive Snakes

Cited by 45 publications

References 7 publications

Image processing and classification algorithm for yeast cell morphology in a microfluidic chip

Image processing and classification algorithm for yeast cell morphology in a microfluidic chip

Snakes on a Plane: A perfect snap for bioimage analysis

Automatic detection and analysis of cell motility in phase‐contrast time‐lapse images using a combination of maximally stable extremal regions and Kalman filter approaches

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