Region Merging Strategy Using Statistical Analysis for Interactive Image Segmentation on Dental Panoramic Radiographs

Arifin, Agus Zainal; Indraswari, Rarasmaya; Suciati, Nanik; Astuti, Eha Renwi; Navastara, Dini Adni

doi:10.15866/irecos.v12i1.10825

Cited by 14 publications

(17 citation statements)

References 24 publications

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“…In this experiment, we compare the proposed method with the interactive image segmentation method proposed in [26] and distance regularized level set evolution (DRLSE) [27]. The interactive image segmentation method uses the mean-shift algorithm to divide the image into regions.…”

Section: Comparison With Semi-automatic Segmentation Methodsmentioning

confidence: 99%

Detection of Overlapping Teeth on Dental Panoramic Radiograph

Arifin¹,

Adam²,

Mohammad³

et al. 2019

IJIES

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Segmentation of single tooth in dental panoramic images is an important process to extract its features and information. However, it might be challenging when the segmentation process faces an overlapping teeth image. In this research, we introduce a new strategy for detecting overlapping area on dental panoramic radiographs automatically. This research proposes automatic thresholding to obtain marking points for the overlapping area and an automatic selection of overlapping area candidates by using the area orientation and the similarity of neighborhood intensity. The experimental results on 44 images show that our proposed strategy can detect overlapping teeth on the dental panoramic radiograph with accuracy, sensitivity, and specificity of 75%, 66.67%, and 85%, respectively. The evaluation conducted on 24 overlapping teeth images shows that the segmentation results of overlapping teeth area have an average misclassification error of 0.31%.

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Section: Comparison With Semi-automatic Segmentation Methodsmentioning

confidence: 99%

Detection of Overlapping Teeth on Dental Panoramic Radiograph

Arifin¹,

Adam²,

Mohammad³

et al. 2019

IJIES

Self Cite

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“…The image can be divided into several homogeneous regions. Some studies also used the mean-shift software to get the initial segmentation as proposed by [3], [9]- [12]. There are two parameters that must be entered for this application is spatial bandwidth (sb) and the color bandwidth (cb).…”

Section: Experiments Analysis Of Region Splittingmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Region Merging using Fuzzy Similarity Measurement on Image Segmentation

Gunawan¹,

Arifin²,

Indraswari³

et al. 2017

IJECE

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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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“…In this research, we improve the previous approach by A. Arifin, et al [5] that developing the hybrid of spatial information and statistical grey level technique to address the ambiguity issue. The fundamental reason is because sometimes the same histogram representative is obtained from the different image types and details.…”

Section: Introductionmentioning

confidence: 99%

Interactive Image Segmentation using Neighborhood Spatial Information and Statistical Grey Level on Dental Panoramic Radiograph

Choirunnisa

Firmanto

Zaenal

2019

Jurnal Ilmu Komputer dan Informasi

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In dental panoramic radiographs, grey-level intensity information has been widely used for object segmentation in digital image. However, low contrast in the radiograph image causes high ambiguity that can cause the inconsistency of classification result. Since the grey-level intensity of background and object is almost similar, so in order to improve the segmentation result, the spatial distance on neighborhod region is applied. In this paper, we proposed a novel strategy to measure the distance using neighborhod spatial information and statistical grey level technique for image segmentation. The proposed method starts by calculating adjacency matrix and measured spatial distance on neighborhood region. Since the value of both distances are not in the same range, then the normalization is needed. The distances merging is approached by tuning the weight using several constant values. The experiment results show that our proposed merging strategy has better segmentation result based on Relative Foreground Area Error value.

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Region Merging Strategy Using Statistical Analysis for Interactive Image Segmentation on Dental Panoramic Radiographs

Abstract: Abstract

Cited by 14 publications

References 24 publications

Detection of Overlapping Teeth on Dental Panoramic Radiograph

Detection of Overlapping Teeth on Dental Panoramic Radiograph

Fuzzy Region Merging using Fuzzy Similarity Measurement on Image Segmentation

Interactive Image Segmentation using Neighborhood Spatial Information and Statistical Grey Level on Dental Panoramic Radiograph

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