A simple segmentation and quantification method for numerical quantitative analysis of cells and tissues.

Background: Microscopic image analysis based on image processing is required for quantitative evaluation of decellularization. Existing methods are not widely used because of expensive commercial software, and machine learning-based techniques lack generality for decellularization because many high-resolution image data has to be processed.
Objective: In this study, we developed an image processing algorithm for quantitative analysis of tissues and cells in a general microscopic image.
Methods: The proposed method extracts the color images obtained by the microscope into reference images consisting of grayscale, red (R), green (G), and blue (B) information and transforms each into a binary image. The transformed images were extracted by separating the cells and tissues through outlier noise elimination, logical multiplication and labeling. In order to verify the method, decellularization of porcine arotic valve was performed by the electrical method. Slice samples were obtained by time and the proposed method was applied.
Results: The experimental results show that the segmentation of cells and tissues, and quantitative analysis of the number of cells and changes in tissue area during the decellularization process was possible.
Conclusions: The proposed method shows that cell and tissue extraction and quantitative numerical analysis were possible in different brightness of microscopic images.