Department of Electrical Engineering and Automation, University of Vaasa, Vaasa, Finland.
Skin Res Technol. 2010 May;16(2):190-7. doi: 10.1111/j.1600-0846.2009.00415.x.
BACKGROUND/PURPOSE: We propose an automatic ulcer segmentation system with a simple manual correction possibility. In addition to visual color information, we use near-infrared (NIR) images because NIR can penetrate deeper into tissue than visual light. The system is able to measure the surface area of a lower extremity ulcer segmented at its different stages and constructs corresponding healing curves over time. This knowledge is useful in monitoring lower extremity ulcers and helps clinicians select the most efficient therapy.
Eighteen lower extremity ulcers and one ulcer on the back were examined from 17 patients. The patients were elderly individuals residing in the long-term care department of the Vaasa city hospital. One of the patients (P14) had been diagnosed with diabetes. The inclusion criteria for patients were an ulcer with a suitable size for the imaging device and the free will to volunteer. We developed a four-band spectral digital camera to image the reflectance of the skin. We use the spectral image pixels, in visual light and NIR, in analysis of lower extremity ulcers. For segmentation, the support vector classifier was found to be the best one. The segmentation system is designed to analyze three main ulcer tissue classes: black/necrotic, yellow/fibrous and red/granulation tissue.
The experiments conducted confirm the feasibility of our approach. In most cases, the computed healing curves correspond to those made manually. The maximum error rate of ulcer area measurement for red/granulation tissue is 33% for 20 cases. This corresponds to the results published in the literature. The black/necrotic tissue may be located deeper under the skin surface; hence, the ulcer boundaries are not well defined, allowing only a rough estimate, yielding a maximum error of 44% for the three cases analyzed. For yellow/fibrous tissue, we had only one image in our database, whose error value is 23%.
We propose a new imaging system for segmentation and measurement of different kinds of ulcers. This system is useful in practice for analysis and measurement of ulcer surface areas and observation of their change over time, which helps clinicians in the treatment of ulcers.
背景/目的：我们提出了一种具有简单手动校正可能性的自动溃疡分割系统。除了视觉颜色信息外，我们还使用近红外（NIR）图像，因为 NIR 可以比可见光更深地穿透组织。该系统能够测量在不同阶段分割的下肢溃疡的表面积，并随时间构建相应的愈合曲线。这些知识有助于监测下肢溃疡，并帮助临床医生选择最有效的治疗方法。
从 17 名患者中检查了 18 个下肢溃疡和一个背部溃疡。患者为居住在瓦萨市医院长期护理部门的老年人。其中一名患者（P14）患有糖尿病。患者的纳入标准是溃疡大小适合成像设备且有自愿参与的意愿。我们开发了一种四波段光谱数字相机来拍摄皮肤的反射率图像。我们在分析下肢溃疡时使用可见光谱和近红外光谱的图像像素。对于分割，支持向量分类器被发现是最佳的。该分割系统旨在分析三种主要的溃疡组织类型：黑色/坏死、黄色/纤维状和红色/肉芽组织。
实验证实了我们方法的可行性。在大多数情况下，计算出的愈合曲线与手动制作的曲线相对应。对于 20 个病例，红色/肉芽组织的溃疡面积测量最大误差率为 33%。这与文献中发表的结果相吻合。黑色/坏死组织可能位于皮肤表面以下更深的位置；因此，溃疡边界定义不明确，仅允许进行粗略估计，对于分析的三个病例，最大误差为 44%。对于黄色/纤维状组织，我们的数据库中只有一个图像，其误差值为 23%。
我们提出了一种新的用于分割和测量不同类型溃疡的成像系统。该系统在实践中对于分析和测量溃疡表面面积以及观察其随时间的变化非常有用，有助于临床医生治疗溃疡。
