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Jurnal SINTA 2

CORTICAL BONE SEGMENTATION USING WATERSHED AND REGION MERGING BASED ON STATISTICAL FEATURES

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 8 No. 2 (2015) · pp. 76–82

DOI
10.21609/jiki.v8i2.305
ISSN
2502-9274, 2088-7051
Published
2015-08-27
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Research on biomedical image is a subject that attracted many researchers’ interest. This is because the biomedical image could contain important information to help analyze a disease. One of the existing researches in his field uses dental panoramic radiographs image to detect osteoporosis. The analyzed area is the width of cortical bone. To analyze that area, however, we need to determine the width of the cortical bone. This requires proper segmentation on the dental panoramic radiographs image. This study proposed the integration of watershed and region merging method based on statistical features for cortical bone segmentation on dental panoramic radiographs. Watershed segmentation process was performed using gradient magnitude value from the input image. The watershed image that still has excess segmentation could be solved by region merging based on statistical features. Statistical features used in this study are mean, standard deviation, and variance. The similarity of adjacent regions is measured using weighted Euclidean distance from the statistical feature of the regions. Merging process was executed by incorporating the background regions as many as possible, while keeping the object regions from being merged. The segmentation result has succeeded in forming the contours of the cortical bone. The average value of accuracy is 93.211%, while the average value of sensitivity and specificity is 93.858% and respectively.

Keywords

Collaboration network of the authors

40 authors · 92 co-authorship links · 11 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Institut Teknologi Sepuluh Nopember
  • Lampung University
  • University of Brawijaya
  • Universitas Islam Negeri Raden Intan Lampung
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Diana PurwitasariInstitut Teknologi Sepuluh Nopember7
Dini Adni NavastaraInstitut Teknologi Sepuluh Nopember4
Rarasmaya IndraswariInstitut Teknologi Sepuluh Nopember3
Munjiah Nur SaadahInstitut Teknologi Sepuluh Nopember2
Rigga Widar AtmagiInstitut Teknologi Sepuluh Nopember2
Dyah S. RahayuInstitut Teknologi Sepuluh Nopember2
Khoirul UmamInstitut Teknologi Sepuluh Nopember2
Gulpi Qorik Oktagalu PratamasunuAffiliation unknown2
Anny YuniartiInstitut Teknologi Sepuluh Nopember2
Darlis HerumurtiInstitut Teknologi Sepuluh Nopember2
Safri AdamInstitut Teknologi Sepuluh Nopember2
Chastine FatichahInstitut Teknologi Sepuluh Nopember2
Wempy Aditya WiryawanAffiliation unknown1
Wawan GunawanUniversitas Islam Negeri Raden Intan Lampung1
⁠Undang RosidinLampung University1
Nina KadaritnaLampung University1
Naser JawasInstitut Teknologi Sepuluh Nopember1
Randy Cahya WihandikaUniversity of Brawijaya1
Go Frendi GunawanInstitut Teknologi Sepuluh Nopember1
Sonny Christiano GosariaInstitut Teknologi Sepuluh Nopember1
Yuita Arum SariUniversity of Brawijaya1
Evy Kamilah RatnasariInstitut Teknologi Sepuluh Nopember1
Siti MutrofinInstitut Teknologi Sepuluh Nopember1
Mutmainnah MuchtarInstitut Teknologi Sepuluh Nopember1
Jayanti Yusmah SariInstitut Teknologi Sepuluh Nopember1
Mohammad ZarkasiInstitut Teknologi Sepuluh Nopember1
Fidi Wincoko PutroInstitut Teknologi Sepuluh Nopember1
Khadijah Fahmi Hayati HolleInstitut Teknologi Sepuluh Nopember1
Andi Baso KaswarInstitut Teknologi Sepuluh Nopember1
Saprina MamaseInstitut Teknologi Sepuluh Nopember1
Saiful Bahri MusaInstitut Teknologi Sepuluh Nopember1
Ahmad Mustofa HadiInstitut Teknologi Sepuluh Nopember1
Arif FadllullahInstitut Teknologi Sepuluh Nopember1
Dasrit Debora KamudiInstitut Teknologi Sepuluh Nopember1
Muhamad NasirInstitut Teknologi Sepuluh Nopember1
Ratri Enggar PaweningAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">CORTICAL BONE SEGMENTATION USING WATERSHED AND REGION MERGING BASED ON STATISTICAL FEATURES</dc:title>
	<dc:creator>Hani`ah, Mamluatul</dc:creator>
	<dc:creator>Aditya, Christian Sri Kusuma</dc:creator>
	<dc:creator>Harto, Aryo</dc:creator>
	<dc:creator>Arifin, Agus Zainal</dc:creator>
	<dc:subject xml:lang="en-US">dental panoramic radiographs</dc:subject>
	<dc:subject xml:lang="en-US">cortical bone</dc:subject>
	<dc:subject xml:lang="en-US">segmentation</dc:subject>
	<dc:subject xml:lang="en-US">watershed</dc:subject>
	<dc:subject xml:lang="en-US">region merging</dc:subject>
	<dc:description xml:lang="en-US">Research on biomedical image is a subject that attracted many researchers’ interest. This is because the biomedical image could contain important information to help analyze a disease. One of the existing researches in his field uses dental panoramic radiographs image to detect osteoporosis. The analyzed area is the width of cortical bone. To analyze that area, however, we need to determine the width of the cortical bone. This requires proper segmentation on the dental panoramic radiographs image. This study proposed the integration of watershed and region merging method based on statistical features for cortical bone segmentation on dental panoramic radiographs. Watershed segmentation process was performed using gradient magnitude value from the input image. The watershed image that still has excess segmentation could be solved by region merging based on statistical features. Statistical features used in this study are mean, standard deviation, and variance. The similarity of adjacent regions is measured using weighted Euclidean distance from the statistical feature of the regions. Merging process was executed by incorporating the background regions as many as possible, while keeping the object regions from being merged. The segmentation result has succeeded in forming the contours of the cortical bone. The average value of accuracy is 93.211%, while the average value of sensitivity and specificity is 93.858% and respectively.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2015-08-27</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/305</dc:identifier>
	<dc:identifier>10.21609/jiki.v8i2.305</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 8 No. 2 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 76 - 82</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v8i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/305/340</dc:relation>
</oai_dc:dc>