Turkmen Hafiza Irem, Karsligil Mine Elif, Kocak Ismail
Computer Engineering Department, Faculty of Electrical & Electronics Engineering, Yildiz Technical University, Istanbul, Turkey.
Otorhinolaryngology Department, Faculty of Medicine, Okan University, Istanbul, Turkey.
Curr Med Imaging Rev. 2019;15(8):785-795. doi: 10.2174/1573405614666180604083854.
Challenges in visual identification of laryngeal disorders lead researchers to investigate new opportunities to help clinical examination. This paper presents an efficient and simple method which extracts and assesses blood vessels on vocal fold tissue in order to serve medical diagnosis.
The proposed vessel segmentation approach has been designed in order to overcome difficulties raised by design specifications of videolaryngostroboscopy and anatomic structure of vocal fold vasculature. The limited number of medical studies on vocal fold vasculature point out that the direction of blood vessels and amount of vasculature are discriminative features for vocal fold disorders. Therefore, we extracted the features of vessels on the basis of these studies. We represent vessels as vascular vectors and suggest a vector field based measurement that quantifies the orientation pattern of blood vessels towards vocal fold pathologies.
In order to demonstrate the relationship between vessel structure and vocal fold disorders, we performed classification of vocal fold disorders by using only vessel features. A binary tree of Support Vector Machine (SVM) has been exploited for classification. Average recall of proposed vessel extraction method was calculated as 0.82 while healthy, sulcus vocalis, laryngitis classification accuracy of 0.75 was achieved.
Obtained success rates showed the efficiency of vocal fold vessels in serving as an indicator of laryngeal diseases.
喉部疾病视觉识别方面的挑战促使研究人员探索有助于临床检查的新方法。本文提出了一种高效且简单的方法，用于提取和评估声带组织上的血管，以辅助医学诊断。
所提出的血管分割方法旨在克服视频喉镜频闪检查的设计规范和声带脉管系统解剖结构所带来的困难。关于声带脉管系统的医学研究数量有限，指出血管方向和脉管系统数量是声带疾病的判别特征。因此，我们基于这些研究提取血管特征。我们将血管表示为血管向量，并提出一种基于向量场的测量方法，以量化血管朝向声带病变的方向模式。
为了证明血管结构与声带疾病之间的关系，我们仅使用血管特征对声带疾病进行分类。利用支持向量机（SVM）二叉树进行分类。所提出的血管提取方法的平均召回率计算为0.82，同时在健康、声带沟、喉炎分类中达到了0.75的准确率。
获得的成功率表明声带血管作为喉部疾病指标的有效性。
