Fuzzy Region Merging using Fuzzy Similarity Measurement on Image Segmentation

Abstract

Some image's regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively. Keyword: Ambiguous region Image segmentation Fuzzy region merging Copyright © 2017 Institute of Advanced Engineering and Science.All rights reserved. Corresponding Author:Wawan Gunawan, Department of Science, Informatics Engineering, Institut Teknologi Sumatera, Bandar Lampung, Indonesia. Email: wawan.gunawan@if.itera.ac.id INTRODUCTIONSegmentation is a basic process in image processing. The purpose of segmentation is to divide the image into regions that have homogenous features or have the same characteristics, e.g., contours, colors, and contrast [1], [2]. In general, image segmentation methods can be divided among three categories, namely automatic, semi-automatic, and manual [3]. Automatic image segmentation methods can be categorized into several groups, namely the histogram-based, edge-based, region-based [4], [5], and hybrid technique [6]. Although automatic segmentation method is fast, optimization process needs to be done to get the optimal parameters that greatly affect the accuracy of automatic segmentation results [7].Automatic segmentation methods have drawbacks when the object and the background region of the image did not have a clear dividing line, causing a difference in perception between the results of the segmentation method and the user's wishes [8]. Semi-automatic segmentation method has been developed to overcome that problem by providing additional information from the user to assist the system in the segmentation process. Under these conditions, our study used a semi-automatic segmentation approach or often referred to as the interactive image segmentation.In interactive image segmentation, user can interact by providing input (user marking) that helps the system in the determination of the object and the background area in the image. Several studies related to semi-automatic segmentation have been proposed by [3], [9]-[12]. Based on those study, interactive image segmentation consist of four main stages. The first stage is dividing the image into several small regions (region splitting) to get the initial segme...

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