Learning-based classification of informative laryngoscopic frames
Learning-based classification of informative laryngoscopic frames
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DOI：
10.1016/j.cmpb.2018.01.030
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发表时间：
2018-05-01
影响因子：
6.1
通讯作者：
Mattos, Leonardo S.
中科院分区：
文献类型：
--
作者：
Moccia, Sara;Vanone, Gabriele O.;Mattos, Leonardo S.
背景和目的：喉癌的早期诊断对降低患者的发病率具有重要意义。窄带成像(NBI)内窥镜通常用于筛查目的，降低了与活检相关的风险，但代价是一些缺点，例如要审查大量数据才能做出诊断。本文的目的是提出一种自动选择信息的内窥镜视频帧的策略，以减少需要处理的数据量并潜在地提高诊断性能。方法：提出一种基于强度、关键点和图像空间内容特征的NBI内窥镜帧分类方法。结果：在18个不同喉镜视频的720幅图像上进行测试时，信息帧的分类召回率达到91%，显著克服了三种最先进的方法(Wilcoxon秩号检验，显著性水平=0.05)。结论：由于该方法对信息帧的识别性能较高，是一种有价值的信息帧选择工具，可用于计算机辅助诊断和内窥镜视角扩展等不同领域。(C)2018爱思唯尔B.V.保留所有权利。
Background and Objective: Early-stage diagnosis of laryngeal cancer is of primary importance to reduce patient morbidity. Narrow-band imaging (NBI) endoscopy is commonly used for screening purposes, reducing the risks linked to a biopsy but at the cost of some drawbacks, such as large amount of data to review to make the diagnosis. The purpose of this paper is to present a strategy to perform automatic selection of informative endoscopic video frames, which can reduce the amount of data to process and potentially increase diagnosis performance.Methods: A new method to classify NBI endoscopic frames based on intensity, keypoint and image spatial content features is proposed. Support vector machines with the radial basis function and the one-versus-one scheme are used to classify frames as informative, blurred, with saliva or specular reflections, or underexposed.Results: When tested on a balanced set of 720 images from 18 different laryngoscopic videos, a classification recall of 91% was achieved for informative frames, significantly overcoming three state of the art methods (Wilcoxon rank-signed test, significance level = 0.05).Conclusions: Due to the high performance in identifying informative frames, the approach is a valuable tool to perform informative frame selection, which can be potentially applied in different fields, such us computer-assisted diagnosis and endoscopic view expansion. (C) 2018 Elsevier B.V. All rights reserved.