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Efficient use of mobile devices for quantification of pressure injury images

机译:有效使用移动设备量化压力损伤图像

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摘要

Pressure Injuries are chronic wounds that are formed due to the constriction of the soft tissues against bone prominences. In order to assess these injuries, the medical personnel carry out the evaluation and diagnosis using visual methods and manual measurements, which can be inaccurate and may generate discomfort in the patients. By using segmentation techniques, the Pressure Injuries can be extracted from an image and accurately parameterized, leading to a correct diagnosis. In general, these techniques are based on the solution of differential equations and the involved numerical methods are demanding in terms of computational resources. In previous work, we proposed a technique developed using toroidal parametric equations for image decomposition and segmentation without solving differential equations. In this paper, we present the development of a mobile application useful for the non-contact assessment of Pressure Injuries based on the toroidal decomposition from images. The usage of this technique allows us to achieve an accurate segmentation almost 8 times faster than Active Contours without Edges (ACWE) and Dynamic Contours methods. We describe the techniques and the implementation for Android devices using Python and Kivy. This application allows for the segmentation and parameterization of injuries, obtain relevant information for the diagnosis and tracking the evolution of patient’s injuries.
机译:压伤是指由于软组织收缩引起的骨突出而形成的慢性伤口。为了评估这些伤害,医务人员使用视觉方法和手动测量进行评估和诊断,这可能是不准确的,并且可能使患者感到不适。通过使用分割技术,可以从图像中提取压力伤害并进行准确的参数设置,从而进行正确的诊断。通常,这些技术基于微分方程的解,并且涉及的数值方法在计算资源方面要求很高。在先前的工作中,我们提出了一种使用环形参数方程开发的技术,用于在不求解微分方程的情况下进行图像分解和分割。在本文中,我们提出了一种移动应用程序的开发,该应用程序可用于基于图像的环面分解的非接触式压力伤害评估。这种技术的使用使我们能够比无边缘主动轮廓(ACWE)和动态轮廓方法快8倍地实现精确分割。我们描述了使用Python和Kivy的Android设备的技术和实现。该应用程序可以对伤害进行分段和参数化,获取有关诊断信息并跟踪患者伤害的发展情况。

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