title: Comparison of contourlet transform and gray level co-occurrence matrix for analyzing cell-scattered patterns.
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.
authors.forename: Jun
authors.initials: J
authors.lastname: Zhang
authors.forename: Gang
authors.initials: G
authors.lastname: Wang
authors.forename: Yuanming
authors.initials: Y
authors.lastname: Feng
authors.forename: Yu
authors.initials: Y
authors.lastname: Sa
mesh_terms: D000465:Algorithms; D048430:Cell Shape; D006801:Humans; D018613:Microscopy, Confocal; D061848:Optical Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*