Journal article
Sequential Active Contour Based on Morphological-Driven Thresholding for Ultrasound Image Segmentation of Ascites
IEEE journal of biomedical and health informatics, Vol.27(9), pp.4305-4316
09/2023
DOI: 10.1109/JBHI.2023.3286869
PMID: 37335794
Abstract
Paracentesis is a high-demanding and routine operation, which has great potentials and benefits if semi-autonomous procedures can be developed. One of the most important techniques that facilitate semi-autonomous paracentesis is to segment the ascites from ultrasound images accurately and efficiently. The ascites, however, is usually with significantly different shapes and noise among different patients, and its shape/size changes dynamically during the paracentesis. This makes most of existing image segmentation methods either time consuming or inaccurate for segmenting ascites from its background. In this paper, we propose a two-stage active contour method to facilitate accurate and efficient segmentation of ascites. First, a morphological-driven thresholding method is developed to locate the initial contour of the ascites automatically. Then, the identified initial contour is fed into a novel sequential active contour algorithm to segment the ascites from background accurately. The proposed method is tested and compared with state-of-the-art active contour methods on over 100 real ultrasound images of ascites, and the results show the superiority of our method in both accuracy and time efficiency.
Details
- Title: Subtitle
- Sequential Active Contour Based on Morphological-Driven Thresholding for Ultrasound Image Segmentation of Ascites
- Creators
- Amirhossein Fallahdizcheh - University of IowaSandeep Laroia - University of IowaChao Wang - University of Iowa
- Resource Type
- Journal article
- Publication Details
- IEEE journal of biomedical and health informatics, Vol.27(9), pp.4305-4316
- DOI
- 10.1109/JBHI.2023.3286869
- PMID
- 37335794
- ISSN
- 2168-2194
- eISSN
- 2168-2208
- Grant note
- DOI: 10.13039/501100008982, name: National Science Foundation, award: 2136298
- Language
- English
- Electronic publication date
- 06/19/2023
- Date published
- 09/2023
- Academic Unit
- Radiology; Industrial and Systems Engineering; Internal Medicine
- Record Identifier
- 9984436287102771
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