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Automated Ultrasound Object Segmentation Using Combinatorial Active Contour Method

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 17 No. 2 (2024) · pp. 203–218

DOI
10.21609/jiki.v17i2.1298
ISSN
2502-9274, 2088-7051
Published
2024-06-04
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Active Contour (AC) is an algorithm widely used in segmentation for developing Computer-Aided Diagnosis (CAD) systems in ultrasound imaging. Existing AC models still retain an interactive nature. This is due to the large number of parameters and coefficients that require manual tuning to achieve stability. Which can result in human error and various issues caused by the inhomogeneity of ultrasound images, such as leakage, false areas, and local minima. In this study, an automatic object segmentation method was developed to assist radiologists in an efficient diagnosis process. The proposed method is called Automatic Combinatorial Active Contour (ACAC), which combines the simplification of the global region-based CV (Chan-Vese) model and improved-GAC (Geodesic Active Contour) for local segmentation. The results of testing with 50 datasets showed an accuracy value of 98.83%, precision of 95.26%, sensitivity of 86.58%, specificity of 99.63%, similarity of 90.58%, and IoU (Intersection over Union) of 82.87%. These quantitative performance metrics demonstrate that the ACAC method is suitable for implementation in a more efficient and accurate CAD system.

Collaboration network of the authors

17 authors · 42 co-authorship links

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

  • State University of Semarang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Agus SuryantoState University of Semarang2
Hanan Iqbal AlrahmaState University of Semarang1
Ahmad Fashiha HastawanState University of Semarang1
Ulfah Mediaty AriefState University of Semarang1
Eliv KurniawanState University of Semarang1
Djoko Adi WidodoAffiliation unknown1
Dania Ayu WulandariState University of Semarang1
Agus MurnomoState University of Semarang1
Bambang SatrioState University of Semarang1
Khoirudin FathoniState University of Semarang1

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">Automated Ultrasound Object Segmentation Using Combinatorial Active Contour Method</dc:title>
	<dc:creator>Anan Nugroho</dc:creator>
	<dc:creator>Sunarko, Budi</dc:creator>
	<dc:creator>Wibawanto, Hari</dc:creator>
	<dc:creator>Mulwinda, Anggraini</dc:creator>
	<dc:creator>Fauzi, Anas</dc:creator>
	<dc:creator>Oktaviyanti, Dwi</dc:creator>
	<dc:creator>Savitri, Dina Wulung</dc:creator>
	<dc:description xml:lang="en-US">Active Contour (AC) is an algorithm widely used in segmentation for developing Computer-Aided Diagnosis (CAD) systems in ultrasound imaging. Existing AC models still retain an interactive nature. This is due to the large number of parameters and coefficients that require manual tuning to achieve stability. Which can result in human error and various issues caused by the inhomogeneity of ultrasound images, such as leakage, false areas, and local minima. In this study, an automatic object segmentation method was developed to assist radiologists in an efficient diagnosis process. The proposed method is called Automatic Combinatorial Active Contour (ACAC), which combines the simplification of the global region-based CV (Chan-Vese) model and improved-GAC (Geodesic Active Contour) for local segmentation. The results of testing with 50 datasets showed an accuracy value of 98.83%, precision of 95.26%, sensitivity of 86.58%, specificity of 99.63%, similarity of 90.58%, and IoU (Intersection over Union) of 82.87%. These quantitative performance metrics demonstrate that the ACAC method is suitable for implementation in a more efficient and accurate CAD system.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2024-06-04</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/1298</dc:identifier>
	<dc:identifier>10.21609/jiki.v17i2.1298</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 17 No. 2 (2024): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 203-218</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v17i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/1298/519</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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