Literature DB >> 19084831

Analysis and recognition of touching cell images based on morphological structures.

Donggang Yu1, Tuan D Pham, Xiaobo Zhou.   

Abstract

Automated analysis and recognition of cell-nuclear phases using fluorescence microscopy images play an important role for high-content screening. A major task of automated imaging based high-content screening is to segment and reconstruct each cell from the touching cell images. In this paper we present new useful method for recognizing morphological structural models of touching cells, detecting segmentation points, determining the number of segmented cells in touching cell image, finding the related data of segmented cell arcs and reconstructing segmented cells. The conceptual frameworks are based on the morphological structures where a series of structural points and their morphological relationships are established. Experiment results have shown the efficient application of the new method for analysis and recognition of touching cell images of high-content screening.

Mesh:

Year:  2008        PMID: 19084831     DOI: 10.1016/j.compbiomed.2008.10.006

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  3 in total

1.  Robust and automated three-dimensional segmentation of densely packed cell nuclei in different biological specimens with Lines-of-Sight decomposition.

Authors:  B Mathew; A Schmitz; S Muñoz-Descalzo; N Ansari; F Pampaloni; E H K Stelzer; S C Fischer
Journal:  BMC Bioinformatics       Date:  2015-06-08       Impact factor: 3.169

2.  Automatic quantitative analysis of morphology of apoptotic HL-60 cells.

Authors:  Yahui Liu; Wang Lin; Xu Yang; Weizi Liang; Jun Zhang; Maobin Meng; John R Rice; Yu Sa; Yuanming Feng
Journal:  EXCLI J       Date:  2014-01-29       Impact factor: 4.068

3.  Automatic detection and counting of urediniospores of Puccinia striiformis f. sp. tritici using spore traps and image processing.

Authors:  Yu Lei; Zhifeng Yao; Dongjian He
Journal:  Sci Rep       Date:  2018-09-11       Impact factor: 4.379

  3 in total

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