A Study on Automated Process for Extracting White Blood Cellular Data from Microscopic Digital Injured Skeletal Muscle Images

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