Automated Quantification of White Blood Cells in Light Microscopic Images of Injured Skeletal Muscle
This work addresses the need for automated analysis of white blood cells in muscle healing for researchers, but it is incremental as it builds on existing thresholding techniques.
The authors tackled the problem of quantifying white blood cells in light microscopic images of injured skeletal muscle by proposing an automated framework based on the Localized Iterative Otsu's threshold method, which achieved better accuracy compared to existing threshold methods in ImageJ.
White blood cells (WBCs) are the most diverse cell types observed in the healing process of injured skeletal muscles. In the course of healing, WBCs exhibit dynamic cellular response and undergo multiple protein expression changes. The progress of healing can be analyzed by quantifying the number of WBCs or the amount of specific proteins in light microscopic images obtained at different time points after injury. In this paper, we propose an automated quantifying and analysis framework to analyze WBCs using light microscopic images of uninjured and injured muscles. The proposed framework is based on the Localized Iterative Otsu's threshold method with muscle edge detection and region of interest extraction. Compared with the threshold methods used in ImageJ, the LI Otsu's threshold method has high resistance to background area and achieves better accuracy. The CD68-positive cell results are presented for demonstrating the effectiveness of the proposed work.