Automatic Torso Detection in Images of Preterm Infants
Automatic Torso Detection in Images of Preterm Infants
复制标题
早产儿图像中的自动躯干检测
DOI：
10.1007/s10916-017-0782-8
复制
发表时间：
2017
影响因子：
5.3
通讯作者：
T. Gale
中科院分区：
文献类型：
--
作者：
Meharmeet Kaur;A. Marshall;C. Eastwood;B. Salmon;P. Dargaville;T. Gale
成像系统在患者呼吸监测中有应用，但在新生儿重症监护病房(NICU)中的应用有限。本文提出了一种在非侵入性呼吸监测中自动检测早产儿躯干的算法。该算法使用归一化切割将每幅图像分割成簇，然后使用两个模糊推理系统来检测尿布和躯干。我们的数据集包括NICU中16名早产儿的头顶图像，光线不受控制，包括姿势的变化、医疗设备的存在和背景的混乱。该算法成功地识别了16幅图像中的15幅躯干区域，检测到的躯干与临床专家识别的躯干具有很高的一致性。
Imaging systems have applications in patient respiratory monitoring but with limited application in neonatal intensive care units (NICU). In this paper we propose an algorithm to automatically detect the torso in an image of a preterm infant during non-invasive respiratory monitoring. The algorithm uses normalised cut to segment each image into clusters, followed by two fuzzy inference systems to detect the nappy and torso. Our dataset comprised overhead images of 16 preterm infants in a NICU, with uncontrolled illumination, and encompassing variations in poses, presence of medical equipment and clutter in the background. The algorithm successfully identified the torso region for 15 of the 16 images, with a high agreement between the detected torso and the torso identified by clinical experts.