2012 9th IEEE International Symposium on Biomedical Imaging (ISBI) 2012
DOI: 10.1109/isbi.2012.6235656
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Coupled edge profile active contours for red blood cell flow analysis

Abstract: Physiological properties of blood flow at the microvasculature scale can be measured by tracking the movement and density of red blood cells (RBCs). In this paper we propose a method for individual RBC segmentation to enable tracking and capturing dynamically varying bulk transport properties. RBCs have varying annular and disk like morphologies, and are often clustered into clumps that are difficult to segment using watershed-based methods. Edge profile active contours in combination with graph coloring based… Show more

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Cited by 28 publications
(26 citation statements)
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“…The long tracks obtained from BCTracker enable characterization of reversal frequencies of cells that would not be possible with short tracklets. BCTracker expands upon our previous work in image-based cell motility analysis [37,45,46]. The key features of BCTracker that can address deformable motion and gliding behaviour across multiple scales ranging from individual bacteria, clusters, and populations are: (i) the use of a of Kalman filter to model bending shape; (ii) use of spatial context through steric relationships between cells to handle high densities and (iii) combined use of active contours with explicit correspondence analysis to support accurate segmentation and tracking of deformable rod-like shapes.…”
Section: Experiments and Trackingmentioning
confidence: 89%
“…The long tracks obtained from BCTracker enable characterization of reversal frequencies of cells that would not be possible with short tracklets. BCTracker expands upon our previous work in image-based cell motility analysis [37,45,46]. The key features of BCTracker that can address deformable motion and gliding behaviour across multiple scales ranging from individual bacteria, clusters, and populations are: (i) the use of a of Kalman filter to model bending shape; (ii) use of spatial context through steric relationships between cells to handle high densities and (iii) combined use of active contours with explicit correspondence analysis to support accurate segmentation and tracking of deformable rod-like shapes.…”
Section: Experiments and Trackingmentioning
confidence: 89%
“…To identify individual fibrils a shape analysis and cluster decomposition module is developed as based on previously reported modules. (Ersoy, Bunyak et al 2012, Sun, Huang et al 2014) Specifically, first connected component labeling was applied to the detection mask and disconnected blobs were identified. Then, to each detected blob B i , an ellipse E i was fitted.…”
Section: Methodsmentioning
confidence: 99%
“…Several methods based on morphological filters and watershed algorithm were proposed to detect cells or cell nuclei in fluorescence images [28][29][30][31][32]. The active contour models [33][34][35][36][37][38][39][40][41][42][43] became popular for automatic cell/nuclei segmentation at the beginning of this century when the classical CV model [33] was proposed. THG images, with Raman and other nonlinear microscopy images, differ from labeled-fluorescence images in their complexity, inherent to their high information density [5,6,20,21,24,25,[44][45][46][47]62].…”
Section: Introductionmentioning
confidence: 99%