Using the Binary Logistic Regression Analysis Function for Image Processing

Binary logistic regression analysis is a statistical method used to predict a set of quantitative and monotonic variables. The logistic regression function is also used in data classification because it does not impose any conditions on the independent variables, so it is a flexible mathematical formula for interpretation that is used to study the relationship between a dependent variable and an explanatory variable. The regression equation is also used to predict the value of the dependent variable at a certain value of the independent variable. In this research, it was proposed to employ the binary logistic linear regression function in image processing as a statistical tool. The purpose is not to analyze the relationship between the dependent variable and the explanatory variable, but rather to develop a tool that works on image segmentation using the threshold technique by considering the highest value of the binary logistic regression vector estimated from the image data as the threshold limit for segmentation. The images were given segmented images containing the most important areas with features that benefit the study, with the removal of non-useful or important areas. And proved its efficiency in extracting all the features of interest in the images.