Hierarchical Clustering Linkage for Region Merging in Interactive Image Segmentation on Dental Cone Beam Computed Tomography

Arifin, Agus Zainal; Maryamah, Maryamah; Arifiani, Siska; Fariza, Arna; Navastara, Dini Adni; Indraswari, Rarasmaya

doi:10.1109/icaiti.2018.8686738

Cited by 3 publications

(7 citation statements)

References 12 publications

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“…This study uses a data set of grayscale images in the form of Cone Beam CT dental images [1], ROI of dental panoramic images [14], and natural images [15]. measure the difference in area between objects, which are contained in the ground truth image and segmented image.…”

Section: Analysis and Resultsmentioning

confidence: 99%

“…The comparison of segmentation results between the proposed method and the Hierarchical Clustering method without multiclass was conducted to determine the effectiveness of the proposed region merging strategy in segmenting. The results of ME and RAE value between previous research [1] and proposed method are shown in Table 4.…”

Section: Analysis and Resultsmentioning

confidence: 99%

“…Distance measurement in this study uses inter-class and intra-class variance [1]. Inter-class variance is used to calculate variances between regions in the image.…”

Section: Multi-class Region Merging With Hierarchical Clusteringmentioning

confidence: 99%

“…Image segmentation can be defined as the process of separating digital images into several parts or series of pixels [1]. Segmentation is the first step in the image analysis process that aims to represent the image into something that is easy to analyze and more meaningful.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Class Region Merging for Interactive Image Segmentation Using Hierarchical Clustering Analysis

Aisyah

Anggraini

Putriwijaya

et al. 2019

Jurnal Ilmu Komputer dan Informasi

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In interactive image segmentation, distance calculation between regions and sequence of region merging is being an important thing that needs to be considered to obtain accurate segmentation results. Region merging without regard to label in Hierarchical Clustering Analysis causes the possibility of two different labels merged into a cluster and resulting errors in segmentation. This study proposes a new multi-class region merging strategy for interactive image segmentation using the Hierarchical Clustering Analysis. Marking is given to regions that are considered as objects and background, which are then referred as classes. A different label for each class is given to prevent any classes with different label merged into a cluster. Based on experiment, the mean value of ME and RAE for the results of segmentation using the proposed method are 0.035 and 0.083, respectively. Experimental results show that giving the label on each class is effectively used in multi-class region merging.

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Section: Analysis and Resultsmentioning

confidence: 99%

Section: Analysis and Resultsmentioning

confidence: 99%

“…Distance measurement in this study uses inter-class and intra-class variance [1]. Inter-class variance is used to calculate variances between regions in the image.…”

Section: Multi-class Region Merging With Hierarchical Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Class Region Merging for Interactive Image Segmentation Using Hierarchical Clustering Analysis

Aisyah

Anggraini

Putriwijaya

et al. 2019

Jurnal Ilmu Komputer dan Informasi

Self Cite

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“…Research on CBCT image segmentation methods has been carried out. Broadly speaking, the segmentation method can be classified into 3 classes namely manual, semi-automatic and automatic segmentation method [18]. In general, the automatic segmentation methods can be classified further into several classes, which are edge-based, threshold, hybrid, and so on.…”

Section: Introductionmentioning

confidence: 99%

Automatic image slice marking propagation on segmentation of dental CBCT

et al. 2019

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Cone Beam Computed Tomography (CBCT) is a radiographic technique that has been commonly used to help doctors provide more detailed information for further examination. Teeth segmentation on CBCT image has many challenges such as low contrast, blurred teeth boundary and irregular contour of the teeth. In addition, because the CBCT produces a lot of slices, in which the neighboring slices have related information, the semi-automatic image segmentation method, that needs manual marking from the user, becomes exhaustive and inefficient. In this research, we propose an automatic image slice marking propagation on segmentation of dental CBCT. The segmentation result of the first slice will be propagated as the marker for the segmentation of the next slices. The experimental results show that the proposed method is successful in segmenting the teeth on CBCT images with the value of Misclassification Error (ME) and Relative Foreground Area Error (RAE) of 0.112 and 0.478, respectively.

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A Tuned Whale Optimization-Based Stacked-LSTM Network for Digital Image Segmentation

et al. 2022

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Hierarchical Clustering Linkage for Region Merging in Interactive Image Segmentation on Dental Cone Beam Computed Tomography

Cited by 3 publications

References 12 publications

Multi-Class Region Merging for Interactive Image Segmentation Using Hierarchical Clustering Analysis

Multi-Class Region Merging for Interactive Image Segmentation Using Hierarchical Clustering Analysis

Automatic image slice marking propagation on segmentation of dental CBCT

A Tuned Whale Optimization-Based Stacked-LSTM Network for Digital Image Segmentation

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