Segmentation of Overlapping Cervical Cells in Normal Pap Smear Images Using Distance-Metric and Morphological Operation

Kurniawan, Rahadian; Muhimmah, Izzati; Kurniawardhani, Arrie; Indrayanti, Indrayanti

doi:10.21512/commit.v11i1.1957

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…The Chan-Vese segmentation method results only consist of the general outline area of the target object, but there are some false contours produced by overlapped object [8]. Overlapping objects also cause the process of interpreting an object to be more difficult [9]. To separate overlapping objects, it is necessary to extract the overlap area first [10].…”

Section: Introductionmentioning

confidence: 99%

Separation of Overlapping Object Segmentation Using Level Set With Automatic Initalization on Dental Panoramic Radiograph

Adam

Arifin

2020

Jurnal Ilmu Komputer dan Informasi

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To extract features on dental objects, it is necessary to segment the teeth. Segmentation is separating between the teeth (objects) with another part than teeth (background). The process of segmenting individual teeth has done a lot of the recently research and obtained good results. However, when faced with overlapping teeth, this is quite challenging. Overlapping tooth segmentation using the latest algorithm produces an object that should be segmented into two objects, instantly becoming one object. This is due to the overlapping between two teeth. To separate overlapping teeth, it is necessary to extract the overlapping object first. Level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. In this study, an automatic initialization strategy is proposed for the level set method to segment overlapping teeth using hierarchical cluster analysis on dental panoramic radiographs images. The proposed strategy was able to initialize overlapping objects properly with accuracy of 73%. Evaluation to measure quality of segmentation result are using misscassification error (ME) and relative foreground area error (RAE). ME and RAE were calculated based on the average results of individual tooth segmentation and obtain 16.41% and 52.14%, respectively. This proposed strategy are expected to be able to help separate the overlapping teeth for human age estimation through dental images in forensic odontology.

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Section: Introductionmentioning

confidence: 99%

Separation of Overlapping Object Segmentation Using Level Set With Automatic Initalization on Dental Panoramic Radiograph

Adam

Arifin

2020

Jurnal Ilmu Komputer dan Informasi

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“…Overlapping objects make the interpretation process more complicated. This problem makes the interpretation process take a lot of time and require highly trained personnel to avoid high error rate [7]. In image segmentation, the overlapping objects could cause inaccurate segmentation result because there are parts of an object that shadow other objects, therefore produces a wrong contour [8].…”

Section: Introductionmentioning

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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