Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction.
Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction.
复制标题
光学断层扫描计算机辅助诊断类风湿性关节炎，第 1 部分：特征提取。
DOI：
10.1117/1.jbo.18.7.076001
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发表时间：
2013
影响因子：
3.5
通讯作者：
Hielscher,AndreasH
中科院分区：
文献类型：
--
作者：
Montejo,LudguierD;Jia,Jingfei;Kim,HyunK;Netz,UweJ;Blaschke,Sabine;Müller,GerhardA;Hielscher,AndreasH
这是关于计算机辅助诊断在扩散光学层析成像（DOT）中的应用的两部分论文的第一部分。提出了一种从DOT图像中提取启发式特征的方法，并利用这些特征诊断类风湿关节炎（RA）。特征提取是第1部分的重点，而五个分类算法的效用在第2部分进行评估。该框架是验证一组219 DOT图像的近端指间关节（PIP）。总的来说，从每个关节的吸收和散射图像中提取了594个特征。三个主要的发现推断。首先，RA受试者的DOT图像与非RA受试者的图像在超过90%的研究特征上存在统计学差异（）。第二，没有可检测到的积液、糜烂或滑膜炎（如通过MRI和超声确定的）的RA受试者的DOT图像与确实表现出积液、糜烂或滑膜炎的RA受试者的DOT图像在统计学上无法区分。因此，该受试者子集可以从DOT图像诊断为RA，而通过MRI或超声图像的审查则无法检测到。第三，散射系数图像产生更好的一维分类器。共有三个特征产生大于0.8的约登指数。这些发现表明DOT可能能够区分健康的PIP关节和受RA影响的关节，有或没有积液，侵蚀或滑膜炎。
This is the first part of a two-part paper on the application of computer-aided diagnosis to diffuse optical tomography (DOT). An approach for extracting heuristic features from DOT images and a method for using these features to diagnose rheumatoid arthritis (RA) are presented. Feature extraction is the focus of Part 1, while the utility of five classification algorithms is evaluated in Part 2. The framework is validated on a set of 219 DOT images of proximal interphalangeal (PIP) joints. Overall, 594 features are extracted from the absorption and scattering images of each joint. Three major findings are deduced. First, DOT images of subjects with RA are statistically different () from images of subjects without RA for over 90% of the features investigated. Second, DOT images of subjects with RA that do not have detectable effusion, erosion, or synovitis (as determined by MRI and ultrasound) are statistically indistinguishable from DOT images of subjects with RA that do exhibit effusion, erosion, or synovitis. Thus, this subset of subjects may be diagnosed with RA from DOT images while they would go undetected by reviews of MRI or ultrasound images. Third, scattering coefficient images yield better one-dimensional classifiers. A total of three features yield a Youden index greater than 0.8. These findings suggest that DOT may be capable of distinguishing between PIP joints that are healthy and those affected by RA with or without effusion, erosion, or synovitis.