Gupta Lalit, Sisodia Rajendra Singh, Pallavi V, Firtion Celine, Ramachandran Ganesan
Philips Reseach Asia - Bangalore.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:7219-22. doi: 10.1109/IEMBS.2011.6091824.
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.
本文提出了一种用于分割胎儿超声图像的新方法。该问题存在各种挑战，包括高噪声、低对比度以及其他超声成像特性，如两个器官/组织的纹理和灰度之间的相似性。在本文中，我们提出了一种基于条件随机场（CRF）的框架来应对分割胎儿超声图像时遇到的挑战。临床上，已知胎儿被特定的母体组织、羊水和胎盘所包围。我们利用这种上下文信息，通过条件随机场来准确分割胎儿图像。所提出的条件随机场框架使用基于小波的纹理特征来表示超声图像，并使用支持向量机（SVM）进行初始标签预测。在有限的真实世界胎儿超声图像数据集上的初步结果很有前景。结果表明，所提出的方法能够处理超声图像中胎儿与其周围环境之间的噪声和相似性问题。
