Sánchez Brea María Luisa, Barreira Rodríguez Noelia, Mosquera González Antonio, Evans Katharine, Pena-Verdeal Hugo
VARPA Group, Department of Computer Science, University of A Coruna, A Coruna, Spain.
Artificial Vision Group, Department of Electronics and Computer Science, University of Santiago de Compostela, Santiago de Compostela, Spain.
Comput Math Methods Med. 2016;2016:3695014. doi: 10.1155/2016/3695014. Epub 2016 Dec 19.
Conjunctival hyperemia or conjunctival redness is a symptom that can be associated with a broad group of ocular diseases. Its levels of severity are represented by standard photographic charts that are visually compared with the patient's eye. This way, the hyperemia diagnosis becomes a nonrepeatable task that depends on the experience of the grader. To solve this problem, we have proposed a computer-aided methodology that comprises three main stages: the segmentation of the conjunctiva, the extraction of features in this region based on colour and the presence of blood vessels, and, finally, the transformation of these features into grading scale values by means of regression techniques. However, the conjunctival segmentation can be slightly inaccurate mainly due to illumination issues. In this work, we analyse the relevance of different features with respect to their location within the conjunctiva in order to delimit a reliable region of interest for the grading. The results show that the automatic procedure behaves like an expert using only a limited region of interest within the conjunctiva.
结膜充血或结膜发红是一种可与多种眼部疾病相关的症状。其严重程度通过标准摄影图表来表示，这些图表通过视觉与患者的眼睛进行比较。这样，充血诊断就成为一项依赖于分级者经验的不可重复的任务。为了解决这个问题，我们提出了一种计算机辅助方法，该方法包括三个主要阶段：结膜分割、基于颜色和血管存在情况在该区域提取特征，以及最后通过回归技术将这些特征转换为分级量表值。然而，结膜分割可能会稍有不准确，主要是由于光照问题。在这项工作中，我们分析了不同特征相对于它们在结膜内位置的相关性，以便为分级划定一个可靠的感兴趣区域。结果表明，自动程序仅使用结膜内有限的感兴趣区域就能像专家一样发挥作用。
