Comparison of Simple Linear Regression and Binary Logistic Linear Regression for Digital Image Segmentation

Abstract

Statistical methods play an important role in image processing. The most important of these methods are the simple linear regression function and the binary logistic regression function, which are used to study the relationship between the dependent variable and the independent variable. They are also used in the process of predicting the value of the dependent variable at a specific value for the independent variable. In this research, the simple linear regression function and the binary logistic regression function were employed in image processing as a statistical technique whose purpose is not to study the relationship between the dependent variable and the independent variable or in the prediction process, but rather a tool that works to segment images using the threshold technique, considering the sum of the simple linear regression vector and the binary logistic linear regression vector, which were estimated from the image data as the threshold limit for segmenting images. The two techniques were good in the segmentation process in terms of giving the best segmented images containing important areas that have features that are useful for the study, while removing the useless or unimportant areas. The two techniques were compared using the Jaccard scale, which is used to determine which technique was better in the segmentation process. It was found that the simple linear regression technique gave a clearer segmented image of the features, and thus it is better than the segmented image using the binary logistic linear regression technique