Segmentation of 2D fetal ultrasound images by exploiting context information using conditional random fields
Abstract
This paper proposes a novel approach for segmenting fetal ultrasound images. This problem presents a variety of challenges including high noise, low contrast, and other US imaging properties such as similarity between texture and gray levels of two organs/ tissues. In this paper, we have proposed a Conditional Random Field (CRF) based framework to handle challenges in segmenting fetal ultrasound images. Clinically, it is known that fetus is surrounded by specific maternal tissues, amniotic fluid and placenta. We exploit this context information using CRFs for segmenting the fetal images accurately. The proposed CRF framework uses wavelet based texture features for representing the ultrasound image and Support Vector Machines (SVM) for initial label prediction. Initial results on a limited dataset of real world ultrasound images of fetus are promising. Results show that proposed method could handle the noise and similarity between fetus and its surroundings in ultrasound images.
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Gupta et al. presented an algorithm for a better separation between the fetus and surrounding information in fetal US, such as maternal tissue, placenta, or amniotic fluid.
“and Gupta et al. presented an algorithm for a better separation between the fetus and surrounding information in fetal US, such as maternal tissue, placenta, or amniotic fluid [177].”
A benefit of automated quality control algorithms was improved US image quality.
“Beside improved US image quality [176,177,178]”
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- Authors
- Gupta, L., Sisodia, R. S., Pallavi, V., Firtion, C., & Ramachandran, G.
- Affiliations
- Philips (Finland) (FI)
- Venue
- 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- Publisher
- IEEE
- Published
- 2011-08
- Volume
- 2011
- Pages
- 7219-7222
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2062815610
- Cited by
- 18
- References
- 7
- Citations by year
- 2023: 2 · 2021: 1 · 2020: 2 · 2019: 1 · 2018: 1 · 2017: 1 · 2015: 4 · 2014: 4 · 2013: 1 · 2012: 1
- FWCI
- 1.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- conditional random fields, contextual information, wavelet texture features, support vector machine, label prediction, amniotic fluid, placenta, noise robustness
- MeSH terms
- Algorithms, Amniotic Fluid, Artificial Intelligence, Automation, Computers, Diagnostic Imaging, Female, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Pattern Recognition, Automated, Pregnancy, Software, Reproducibility of Results, Models, Statistical, Ultrasonography, Prenatal, Support Vector Machine
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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