21 July 2017 A multivariate shape quantification approach for sickle red blood cell in patient-specific microscopy image data
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104203W (2017) https://doi.org/10.1117/12.2281565
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
Abstract
The morphological change of red blood cells(RBCs) plays an important role in revealing the biomechanical and biorheological characteristics of RBCs. Aiming to extract the shape indices for the sickle RBCs, an automated ex-vivo RBC shape quantification method is proposed. First, single RBC regions (ROIs) are extracted from raw microscopy image via an automatic hierarchical ROI extraction method. Second, an improved random walk method is used to detect the RBC outline. Finally, three types of RBC shape factors are calculated based on the elliptical fitting RBC contour. Experiments indicate that the proposed method can accurately segment the RBCs from the microscopy images with low contrast and prevent the disturbance of artifacts. Moreover, it can provide an efficient shape quantification means for diverse RBC shapes in a batch manner.
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Mengjia Xu, Mengjia Xu, Jinzhu Yang, Jinzhu Yang, Hong Zhao, Hong Zhao, } "A multivariate shape quantification approach for sickle red blood cell in patient-specific microscopy image data", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104203W (21 July 2017); doi: 10.1117/12.2281565; https://doi.org/10.1117/12.2281565
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