Automated measurement of pressure injury through image processing
Automated measurement of pressure injury through image processing
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DOI：
10.1111/jocn.13726
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发表时间：
2017-11-01
影响因子：
4.2
通讯作者：
Mathews, Carol
中科院分区：
文献类型：
--
作者：
Li, Dan;Mathews, Carol
目的和目标。目的：利用护理文件中的电子压伤图像，开发一种自动测量压伤的图像处理算法。帕拉;&帕拉;背景。拍摄压力伤并将图像存储在电子健康记录中是许多医院的标准做法。然而，压力损伤的手动测量是耗时的、具有挑战性的，并且受到具有压力损伤和临床环境的复杂性的阅片者内/阅片者间可变性的影响。帕拉;&帕拉;Design.一个横截面算法开发研究。帕拉;&帕拉;方法。从宾夕法尼亚州西部的一家医院获得了一组32张压伤图像。首先，我们将图像从RGB（即红色，绿色和蓝色）颜色空间转换为YCbCr颜色空间，以消除来自不同光照条件和肤色的推断。其次，概率图，由肤色高斯模型生成，指导使用支持向量机分类器的压力损伤分割过程。第三，在分割之后，包括在每个图像中的参考标尺使得能够进行透视变换并确定压力损伤大小。最后由两名护士独立测量32幅压伤图像，计算类内相关系数。帕拉;&帕拉;结果。提出了一种基于图像处理的压力伤尺寸自动测量方法。评分者间和评分者内分析都取得了良好的可靠性。&帕拉;&帕拉;结论。对压力损伤大小测量的验证（1）表明我们的图像处理算法是通过临床压力损伤图像监测压力损伤进展的可靠方法，（2）为压力损伤评估和记录提供了新的见解。帕拉;&帕拉;与临床实践相关。一旦我们的算法得到进一步发展，临床医生可以提供一个客观，可靠和有效的计算工具，分割和测量的压力伤害。有了这个，临床医生将能够更有效地监测压力损伤的愈合过程。
Aims and objectives. To develop an image processing algorithm to automatically measure pressure injuries using electronic pressure injury images stored in nursing documentation.& para;& para;Background. Photographing pressure injuries and storing the images in the electronic health record is standard practice in many hospitals. However, the manual measurement of pressure injury is time-consuming, challenging and subject to intra/inter-reader variability with complexities of the pressure injury and the clinical environment.& para;& para;Design. A cross-sectional algorithm development study.& para;& para;Methods. A set of 32 pressure injury images were obtained from a western Pennsylvania hospital. First, we transformed the images from an RGB (i.e. red, green and blue) colour space to a YCbCr colour space to eliminate inferences from varying light conditions and skin colours. Second, a probability map, generated by a skin colour Gaussian model, guided the pressure injury segmentation process using the Support Vector Machine classifier. Third, after segmentation, the reference ruler - included in each of the images - enabled perspective transformation and determination of pressure injury size. Finally, two nurses independently measured those 32 pressure injury images, and intra class correlation coefficient was calculated.& para;& para;Results. An image processing algorithm was developed to automatically measure the size of pressure injuries. Both inter- and intra-rater analysis achieved good level reliability.& para;& para;Conclusions. Validation of the size measurement of the pressure injury (1) demonstrates that our image processing algorithm is a reliable approach to monitoring pressure injury progress through clinical pressure injury images and (2) offers new insight to pressure injury evaluation and documentation.& para;& para;Relevance to clinical practice. Once our algorithm is further developed, clinicians can be provided with an objective, reliable and efficient computational tool for segmentation and measurement of pressure injuries. With this, clinicians will be able to more effectively monitor the healing process of pressure injuries.