Band-Selection of a Portal LED-Induced Autofluorescence Multispectral Imager to Improve Oral Cancer Detection.
Band-Selection of a Portal LED-Induced Autofluorescence Multispectral Imager to Improve Oral Cancer Detection.
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DOI：
10.3390/s21093219
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发表时间：
2021-05-06
期刊：
影响因子：
--
通讯作者：
Ou-Yang M
中科院分区：
文献类型：
--
作者：
Yan YJ;Cheng NL;Jan CI;Tsai MH;Chiou JC;Ou-Yang M
本研究的目的是寻找有效的光谱波段，用于口腔癌的早期检测。使用自制的便携式发光二极管（LED）诱导的自发荧光多光谱成像仪，配备有365和405 nm的激发LED，发射滤波器的中心波长为470，505，525，532，550，595，632，635，和695 nm，和彩色图像传感器，在不同波段的光谱图像。在中国医科大学附属医院的离体试验中，收集了62名健康参与者的218个健康点和62名患者的218个肿瘤点的光谱图像。这些离体试验与体内试验相似，因为解剖样本的光谱图像在现场肿瘤切除后立即采集。分别采用4种计算方法对红、蓝、绿色滤光片与9个发射滤光片相关和不相关时的光谱图像进行量化，包括总强度、最高强度级数、熵和分维。四种计算方法的组合，两种激发光源，两种强度，和30个光谱波段在三个实验中形成264个分类器。每个分类器中的量化数据分为两组：一组是优化量化数据阈值的训练组，另一组是在此优化阈值下测试的验证组。每个分类器的灵敏度、特异性和准确性来自这些测试。为了根据区域下的面积和测试结果来识别有影响的光谱带，使用了单层网络学习过程。与传统的基于规则的方法进行了比较，以显示其上级和更快的性能。因此，通过基于AI的方法选择了中心波长为470、505、532和550 nm的四个发射滤波器，并使用基于规则的方法进行了验证。使用这些发射过滤器的六个分类器的灵敏度更显着超过90%。其平均敏感性约为96.15%，平均特异性约为69.55%，平均准确性约为82.85%。
This aim of this study was to find effective spectral bands for the early detection of oral cancer. The spectral images in different bands were acquired using a self-made portable light-emitting diode (LED)-induced autofluorescence multispectral imager equipped with 365 and 405 nm excitation LEDs, emission filters with center wavelengths of 470, 505, 525, 532, 550, 595, 632, 635, and 695 nm, and a color image sensor. The spectral images of 218 healthy points in 62 healthy participants and 218 tumor points in 62 patients were collected in the ex vivo trials at China Medical University Hospital. These ex vivo trials were similar to in vivo because the spectral images of anatomical specimens were immediately acquired after the on-site tumor resection. The spectral images associated with red, blue, and green filters correlated with and without nine emission filters were quantized by four computing method, including summated intensity, the highest number of the intensity level, entropy, and fractional dimension. The combination of four computing methods, two excitation light sources with two intensities, and 30 spectral bands in three experiments formed 264 classifiers. The quantized data in each classifier was divided into two groups: one was the training group optimizing the threshold of the quantized data, and the other was validating group tested under this optimized threshold. The sensitivity, specificity, and accuracy of each classifier were derived from these tests. To identify the influential spectral bands based on the area under the region and the testing results, a single-layer network learning process was used. This was compared to conventional rules-based approaches to show its superior and faster performance. Consequently, four emission filters with the center wavelengths of 470, 505, 532, and 550 nm were selected by an AI-based method and verified using a rule-based approach. The sensitivities of six classifiers using these emission filters were more significant than 90%. The average sensitivity of these was about 96.15%, the average specificity was approximately 69.55%, and the average accuracy was about 82.85%.
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