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

Abstract

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 cellscattered images.

Single‐Particle Characterization by Elastic Light Scattering

“…compared the contourlet transform with the GLCM—the latter showed better results. [ 254 ] Recently Xie et al. [ 107 ] used the gray‐level differential statistics and the SVM with leave‐one‐out cross‐validation to identification of the acute and chronic myeloid leukemic cells, achieving sensitivity of 92% and a specificity of 95%.…”