Comparison of contourlet transform and gray level co-occurrence matrix for analyzing cell-scattered patterns

Jun Zhang; Gang Wang; Yuanming Feng; Yu Sa

doi:10.1117/1.JBO.21.8.086013

23 August 2016 Comparison of contourlet transform and gray level co-occurrence matrix for analyzing cell-scattered patterns

Jun Zhang, Gang Wang, Yuanming Feng, Yu Sa

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Journal of Biomedical Optics, Vol. 21, Issue 8, 086013 (August 2016). https://doi.org/10.1117/1.JBO.21.8.086013

Abstract

Distribution of scattered image patterns hinges on morphological and optical characteristics of cells. This paper applied a numerical method to simulate scattered images of real cell morphologies, which were reconstructed from confocal image stacks dyed by fluorescent stains. Two approaches, contourlet transform (CT) and gray level co-occurrence matrix (GLCM), were then used to analyze the simulated scattered images. The results showed that features extracted using GLCM contained more information than those extracted using CT. Higher classification accuracy could be achieved with a single GLCM parameter than CT and GLCM could achieve higher accuracy with fewer parameters than CT when using multiple parameters. Meanwhile, GLCM requires less computational cost. Thus, GLCM is more suitable and efficient than CT for the analysis of cell-scattered images.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Jun Zhang, Gang Wang, Yuanming Feng, and Yu Sa "Comparison of contourlet transform and gray level co-occurrence matrix for analyzing cell-scattered patterns," Journal of Biomedical Optics 21(8), 086013 (23 August 2016). https://doi.org/10.1117/1.JBO.21.8.086013

Published: 23 August 2016

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