Automated Ultrasound Object Segmentation Using Combinatorial Active Contour Method
Dublin Core
Title
Automated Ultrasound Object Segmentation Using Combinatorial Active Contour Method
Subject
Active contour, automated, CAD, segmentation, US
Description
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
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
Creator
Anan Nugroho, Budi Sunarko, Hari Wibawanto, Anggraini Mulwinda, Anas Fauzi, Dwi Oktaviyanti, Dina Wulung Savitri
Source
http://dx.doi.org/10.21609/jiki.v17i2.1298
Publisher
Faculty of Computer Science Universitas Indonesia
Date
2024-06-04
Contributor
Sri Wahyuni
Rights
e-ISSN : 2502-9274 printed ISSN : 2088-7051
Format
PDF
Language
English
Type
Text
Coverage
Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Files
Collection
Citation
Anan Nugroho, Budi Sunarko, Hari Wibawanto, Anggraini Mulwinda, Anas Fauzi, Dwi Oktaviyanti, Dina Wulung Savitri, “Automated Ultrasound Object Segmentation Using Combinatorial Active Contour Method,” Repository Horizon University Indonesia, accessed May 22, 2025, https://repository.horizon.ac.id/items/show/8894.