Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, HR-31000 Osijek, Croatia.
Sensors (Basel). 2023 Mar 21;23(6):3298. doi: 10.3390/s23063298.
Chronic wounds, are a worldwide health problem affecting populations and economies as a whole. With the increase in age-related diseases, obesity, and diabetes, the costs of chronic wound healing will further increase. Wound assessment should be fast and accurate in order to reduce possible complications and thus shorten the wound healing process. This paper describes an automatic wound segmentation based on a wound recording system built upon a 7-DoF robot arm with an attached RGB-D camera and high-precision 3D scanner. The developed system represents a novel combination of 2D and 3D segmentation, where the 2D segmentation is based on the MobileNetV2 classifier and the 3D component is based on the active contour model, which works on the 3D mesh to further refine the wound contour. The end output is the 3D model of only the wound surface without the surrounding healthy skin and geometric parameters in the form of perimeter, area, and volume.
慢性伤口是一个全球性的健康问题,影响着整个人口和整个经济。随着与年龄相关的疾病、肥胖和糖尿病的增加,慢性伤口愈合的成本将进一步增加。为了减少可能的并发症,从而缩短伤口愈合过程,伤口评估应该快速而准确。本文描述了一种基于伤口记录系统的自动伤口分割方法,该系统建立在带有 RGB-D 相机和高精度 3D 扫描仪的 7-DoF 机械臂上。所开发的系统代表了 2D 和 3D 分割的新组合,其中 2D 分割基于 MobileNetV2 分类器,3D 部分基于主动轮廓模型,该模型作用于 3D 网格以进一步细化伤口轮廓。最终输出是仅包括伤口表面的 3D 模型,而不包括周围健康皮肤和周长、面积和体积等几何参数。