Award Date

August 2017

Degree Type


Degree Name

Master of Science in Electrical Engineering (MSEE)


Electrical and Computer Engineering

First Committee Member

Mei Yang

Second Committee Member

Emma Regentova

Third Committee Member

Yingtao Jiang

Fourth Committee Member

Barbara S. Schneider

Number of Pages



Muscle regeneration process tracking and analysis aim to monitor the injured muscle tissue section over time and analyze the muscle healing procedure. In this procedure, as one of the most diverse cell types observed, white blood cells (WBCs) exhibit dynamic cellular response and undergo multiple protein expression changes. The characteristics, amount, location, and distribution compose the action of cells which may change over time. Their actions and relationships over the whole healing procedure can be analyzed by processing the microscopic images taken at different time points after injury. The previous studies of muscle regeneration usually employ manual approach or basic intensity process to detect and count WBCs. In comparison, computer vision method is more promising in accuracy, processing speed, and labor cost. Besides, it can extract features like cell/cluster size and eccentricity fast and accurately.

In this thesis, we propose an automated quantifying and analysis framework to analyze the WBC in light microscope images of uninjured and injured skeletal muscles. The proposed framework features a hybrid image segmentation method combining the Localized Iterative Otsu’s threshold method assisted by neural networks classifiers and muscle edge detection. In specific, both neural network and convoluted neural network based classifiers are studied and compared. Via this framework, the CD68-positive WBC and 7/4-positive WBC quantification and density distribution results are analyzed for demonstrating the effectiveness of the proposed method.


muscle healing; neural network; quantification; segmentation; white blood cell


Computer Engineering | Electrical and Computer Engineering

File Format


Degree Grantor

University of Nevada, Las Vegas




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