title: 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.
authors.affiliation: Philips Reseach Asia - Bangalore. lalit.gupta@philips.com
authors.forename: Lalit
authors.initials: L
authors.lastname: Gupta
authors.forename: Rajendra Singh
authors.initials: RS
authors.lastname: Sisodia
authors.forename: V
authors.initials: V
authors.lastname: Pallavi
authors.forename: Celine
authors.initials: C
authors.lastname: Firtion
authors.forename: Ganesan
authors.initials: G
authors.lastname: Ramachandran
mesh_terms: D000465:Algorithms; D000653:Amniotic Fluid; D001185:Artificial Intelligence; D001331:Automation; D003201:Computers; D003952:Diagnostic Imaging / Q000379:methods; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011247:Pregnancy; D015203:Reproducibility of Results; D012984:Software; D060388:Support Vector Machine; D016216:Ultrasonography, Prenatal / Q000379:methods*