SEGMENTASI TAMBAL GIGI (FILLING) PADA DENTAL PANORAMIC XRAY MENGGUNAKAN METODE MULTIPLE MORPHOLOGICAL GRADIENT (MMG)

WAHYUNI, MEILY and Passarella, Rossi (2020) SEGMENTASI TAMBAL GIGI (FILLING) PADA DENTAL PANORAMIC XRAY MENGGUNAKAN METODE MULTIPLE MORPHOLOGICAL GRADIENT (MMG). Undergraduate thesis, Sriwijaya University.

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Abstract

Segmentation is an image processing that can separate the area of objects with unwanted areas or foreground and background by distinguishing the intensity of the gray level pixels on an object from the neighboring gray level. This study discusses for filling segmentation using 116 Dental Panoramic Xray images as a trial input image. The results of these 116 images obtained as dental problems with the type of crown, bridge and filling (dental fillings). In the first step, the image will be converted into grayscale process, then the image which will be contrasted by contrast stretching process, as well as using the morphological filtering of the dilation and erosion and continued by detecting the edge of the gradient morphology to improve image quality. Next will be segmented with the multiple morphological gradient method and the final step is imcomplent. In this study, the percentage accuracy obtained from the dataset based on the expert is equal to 70.69% of the dataset, while it is obtained from the results of the program that is equal to 61.20% of the dataset. Which means, only 9.49% fishing between the results of the expert with the results of the study. So, based on the results obtained, the method intended can be used for dental patching segmentation on Dental Panoramic Xray.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	gigi, dental panoramic x ray, multiple morpological gradient, segmentasi
Subjects:	R Medicine > RK Dentistry > RK280 Oral and dental anatomy and physiology T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics > TA1632.A48 Image processing.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Users 6788 not found.
Date Deposited:	30 Jul 2020 08:01
Last Modified:	30 Jul 2020 08:01
URI:	http://repository.unsri.ac.id/id/eprint/31976

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