New Algorithm to Extract Centerline of 2D Objects Based on Clustering

Ferchichi, Salah; Wang, Shengrui; Grira, Sofiane

doi:10.1007/978-3-540-74260-9_33

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…The most common approaches for centreline detection are based on Euclidean distance transform [30,31], fire propagation [32], Voronoi diagrams [33], clustering [34] and hybrid methods [35,36]. …”

Section: Methodsmentioning

confidence: 99%

Bacterial cell identification in differential interference contrast microscopy images

et al. 2013

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BackgroundMicroscopy image segmentation lays the foundation for shape analysis, motion tracking, and classification of biological objects. Despite its importance, automated segmentation remains challenging for several widely used non-fluorescence, interference-based microscopy imaging modalities. For example in differential interference contrast microscopy which plays an important role in modern bacterial cell biology. Therefore, new revolutions in the field require the development of tools, technologies and work-flows to extract and exploit information from interference-based imaging data so as to achieve new fundamental biological insights and understanding.ResultsWe have developed and evaluated a high-throughput image analysis and processing approach to detect and characterize bacterial cells and chemotaxis proteins. Its performance was evaluated using differential interference contrast and fluorescence microscopy images of Rhodobacter sphaeroides.ConclusionsResults demonstrate that the proposed approach provides a fast and robust method for detection and analysis of spatial relationship between bacterial cells and their chemotaxis proteins.

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Section: Methodsmentioning

confidence: 99%

Bacterial cell identification in differential interference contrast microscopy images

et al. 2013

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Curve Skeletons of Planar Domains

Eftekharian¹,

Ilieş²

2011

Computer-Aided Design and Applications

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CAD has been traditionally used to assist in engineering design and modeling for representation, analysis and manufacturing. Advances in Information Technology and in Biomedicine have created new uses for CAD with many novel and important biomedical applications. Such applications can be found, for example, in the design and modeling of orthopedics, medical implants, and tissue modeling in which CAD can be used to describe the morphology, heterogeneity, and organizational structure of tissue and anatomy. CAD has also played an important role in computer-aided tissue engineering for biomimetic design, analysis, simulation and freeform fabrication of tissue scaffolds and substitutes. This special issue selected 6 referred papers as an introduction to recent technical advances, research and developments, and novel applications on using CAD modeling, design, analysis, and freeform fabrication for biomedical and tissue engineering.A biomodeling approach to compute accurate contours from non-invasive medical imaging data using B-spline curve approximation was introduced in the first paper. The approach and the visualization were conducted primarily based on the voxel and facet (triangle) based model representation. The NURBS, the de facto standard to represent geometry in CAD systems, was used in the reported work to perform a critical task for bio-fabrication of human bone and head. The modeling accuracy and shape fidelity were commented in the report.

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