Fuzzy Region Merging With Hierarchical Clustering to Find Optimal Initialization of Fuzzy Region in Image Segmentation

Abstract

One of the most important goals in image segmentation is the process of separating the object parts from the image background. Image segmentation is also a fundamental stage in the development of other image applications such as object recognition, target tracking, computer vision, and biomedical image processing. Interactive image segmentation methods with additional user interaction are still popular in research. Interactive image segmentation aims to provide additional information through simple interactions, especially in images with complex objects. Interactive image segmentation with region merging processes has drawbacks, one of which is suboptimal region splitting due to soft color shades, blurred contours, and uneven lighting, referred to in this study as ambiguous regions. However, in the fuzzy region initialization stage after obtaining values from the marker process, there is a possibility of missing or suboptimal determination of fuzzy regions. This is because it only takes the highest gray level value for the background marker and the lowest gray level value for the object marker. In this study, fuzzy region merging using hierarchical clustering is proposed to find optimal initialization for fuzzy regions in image segmentation. Based on the experimental results, the proposed method can achieve optimal segmentation with an average misclassification error value of 2.62% for Natural Images and 9.33% for Dental Images.