Verikas A, Gelzinis A, Bacauskiene M, Uloza V
Department of Applied Electronics, Kaunas University of Technology, LT-3031, Kaunas, Lithuania.
Artif Intell Med. 2006 Jan;36(1):71-84. doi: 10.1016/j.artmed.2004.11.001.
The objective of this work is to investigate a possibility of creating a computer-aided decision support system for an automated analysis of vocal cord images aiming to categorize diseases of vocal cords.
The problem is treated as a pattern recognition task. To obtain a concise and informative representation of a vocal cord image, colour, texture, and geometrical features are used. The representation is further analyzed by a pattern classifier categorizing the image into healthy, diffuse, and nodular classes.
The approach developed was tested on 785 vocal cord images collected at the Department of Otolaryngology, Kaunas University of Medicine, Lithuania. A correct classification rate of over 87% was obtained when categorizing a set of unseen images into the aforementioned three classes.
Bearing in mind the high similarity of the decision classes, the results obtained are rather encouraging and the developed tools could be very helpful for assuring objective analysis of the images of laryngeal diseases.
本研究旨在探讨创建一个计算机辅助决策支持系统的可能性，该系统用于对声带图像进行自动分析，以对声带疾病进行分类。
该问题被视为一个模式识别任务。为了获得声带图像简洁且信息丰富的表示，使用了颜色、纹理和几何特征。通过模式分类器对该表示进行进一步分析，将图像分为健康、弥漫性和结节性类别。
所开发的方法在立陶宛考纳斯医科大学耳鼻喉科收集的785张声带图像上进行了测试。当将一组未见图像分类为上述三个类别时，获得了超过87%的正确分类率。
考虑到决策类别之间的高度相似性，所获得的结果相当令人鼓舞，并且所开发的工具对于确保对喉部疾病图像进行客观分析可能非常有帮助。
