Skeletal muscle injury is one of the common injuries caused by high-intensity sports activities, military related works, and natural disasters. In order to discover better therapies, it is important to study muscle regeneration process. Muscle regeneration process tracking is the act of monitoring injured tissue section over time, noting white blood cell behavior and cell-fiber relations. A large number of microscopic images are taken for tracking muscle regeneration process over multiple time instances. Currently, manual approach is widely used to analyze a microscopic image of muscle cross section, which is time consuming, tedious and buggy.
Automation of this research methodology is essential to process a big amount of data. The objective of this thesis is to develop a framework to track the regeneration process automatically. The framework includes dynamic thresholding, morphological processing, and feature extraction.Based on the clinical assumptions, the threshold is calculated using standard deviation and mean of probable single cells. After thresholding, six parameters are calculated including average size, cell count, cell area density, cell count on the basis of size, the number of cytoplasmic and membrane cells as well as the average distance between cellular objects. All these parameters are estimated over the time, which helped to note the pattern of change in leukocytes (White blood cells) presence. Based on these results, a clear pattern of leukocyte movement is observed. Our future work includes deriving the mathematical equations using regression model on the data set of increased time points
