Segmentation of 2D fetal ultrasound images by exploiting context information using conditional random fields
| Autor: | Rajendra Singh Sisodia, Lalit Gupta, Celine Firtion, Ganesan Ramachandran, V. Pallavi |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Diagnostic Imaging
Conditional random field Engineering Support Vector Machine Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Context (language use) Ultrasonography Prenatal Pattern Recognition Automated Automation Artificial Intelligence Pregnancy Image Interpretation Computer-Assisted Image Processing Computer-Assisted Medical imaging Humans Computer vision Segmentation Models Statistical Computers business.industry Reproducibility of Results Image segmentation Amniotic Fluid Support vector machine embryonic structures Female Artificial intelligence Noise (video) business Algorithms Software |
| Zdroj: | EMBC |
| DOI: | 10.1109/iembs.2011.6091824 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|