David Ortiz P, Sierra-Sosa Daniel, Zapirain Begoña García
Mathematical Modeling Research Group, School of Sciences, Universidad EAFIT, Carrera 49 NO 7 Sur-50, Medellín, Colombia.
DeustoTech - Fundación Deusto, Avda/Universidades 24, 48007, Bilbao, Spain.
Biomed Eng Online. 2017 Jan 6;16(1):4. doi: 10.1186/s12938-016-0298-3.
Pressure ulcers have become subject of study in recent years due to the treatment high costs and decreased life quality from patients. These chronic wounds are related to the global life expectancy increment, being the geriatric and physical disable patients the principal affected by this condition. Injuries diagnosis and treatment usually takes weeks or even months by medical personel. Using non-invasive techniques, such as image processing techniques, it is possible to conduct an analysis from ulcers and aid in its diagnosis.
This paper proposes a novel technique for image segmentation based on contrast changes by using synthetic frequencies obtained from the grayscale value available in each pixel of the image. These synthetic frequencies are calculated using the model of energy density over an electric field to describe a relation between a constant density and the image amplitude in a pixel. A toroidal geometry is used to decompose the image into different contrast levels by variating the synthetic frequencies. Then, the decomposed image is binarized applying Otsu's threshold allowing for obtaining the contours that describe the contrast variations. Morphological operations are used to obtain the desired segment of the image.
The proposed technique is evaluated by synthesizing a Data Base with 51 images of pressure ulcers, provided by the Centre IGURCO. With the segmentation of these pressure ulcer images it is possible to aid in its diagnosis and treatment. To provide evidences of technique performance, digital image correlation was used as a measure, where the segments obtained using the methodology are compared with the real segments. The proposed technique is compared with two benchmarked algorithms. The results over the technique present an average correlation of 0.89 with a variation of ±0.1 and a computational time of 9.04 seconds.
The methodology presents better segmentation results than the benchmarked algorithms using less computational time and without the need of an initial condition.
近年来，由于治疗成本高昂以及患者生活质量下降，压疮已成为研究对象。这些慢性伤口与全球预期寿命的增加有关，老年患者和身体残疾患者是受此状况影响的主要群体。医疗人员对损伤的诊断和治疗通常需要数周甚至数月时间。使用图像处理技术等非侵入性技术，可以对溃疡进行分析并辅助其诊断。
本文提出了一种基于对比度变化的图像分割新技术，该技术利用从图像每个像素的灰度值中获得的合成频率。这些合成频率是使用电场能量密度模型计算得出的，以描述恒定密度与像素中图像幅度之间的关系。采用环形几何结构，通过改变合成频率将图像分解为不同的对比度级别。然后，应用大津阈值对分解后的图像进行二值化处理，从而获得描述对比度变化的轮廓。使用形态学操作来获取图像的所需部分。
通过合成由IGURCO中心提供的包含51张压疮图像的数据库，对所提出的技术进行了评估。通过对这些压疮图像进行分割，可以辅助其诊断和治疗。为了提供技术性能的证据，使用数字图像相关性作为一种度量方法，将使用该方法获得的部分与真实部分进行比较。将所提出的技术与两种基准算法进行了比较。该技术的结果显示平均相关性为0.89，变化范围为±0.1，计算时间为9.04秒。
该方法比基准算法呈现出更好的分割结果，使用的计算时间更少，并且无需初始条件。
