Computer-aided interpretation approach for optical tomographic images.
Computer-aided interpretation approach for optical tomographic images.
复制标题
光学断层图像的计算机辅助解释方法。
DOI：
10.1117/1.3516705
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发表时间：
2010
影响因子：
3.5
通讯作者：
Hielscher,AndreasH
中科院分区：
文献类型：
--
作者：
Klose,ChristianD;Klose,AlexanderD;Netz,UweJ;Scheel,AlexanderK;Beuthan,Jurgen;Hielscher,AndreasH
提出了一种利用光学层析图像检测人体手指关节风湿性关节炎（RA）的计算机辅助解释方法。图像解释方法采用一种分类算法，该算法利用所谓的自组织映射方案将手指分为受RA影响或未受RA影响的手指。与之前的研究不同，这允许结合多个图像特征，例如最小值和最大值的吸收系数，以识别受影响和未受影响的关节。从敏感性、特异性、约登指数和互信息等方面评价了该方法的分类性能。不同的方法（即临床诊断、超声成像、磁共振成像和光学层析成像检查）被用来产生地面真实基准，以确定图像解释的性能。使用来自100个手指关节的数据，研究结果表明，与先前研究中采用的单参数分类相比，一些参数组合导致更高的敏感性，而另一些参数组合导致更高的特异性。当吸收系数的最小/最大比值与图像方差相结合时，可以达到最佳性能。在这种情况下，灵敏度和特异性可以达到0.9以上。这些值远远高于仅使用单一参数分类时获得的值，其中灵敏度和特异性仍然远低于0.8。
A computer-aided interpretation approach is proposed to detect rheumatic arthritis (RA) in human finger joints using optical tomographic images. The image interpretation method employs a classification algorithm that makes use of a so-called self-organizing mapping scheme to classify fingers as either affected or unaffected by RA. Unlike in previous studies, this allows for combining multiple image features, such as minimum and maximum values of the absorption coefficient for identifying affected and not affected joints. Classification performances obtained by the proposed method were evaluated in terms of sensitivity, specificity, Youden index, and mutual information. Different methods (i.e., clinical diagnostics, ultrasound imaging, magnet resonance imaging, and inspection of optical tomographic images), were used to produce ground truth benchmarks to determine the performance of image interpretations. Using data from 100 finger joints, findings suggest that some parameter combinations lead to higher sensitivities, while others to higher specificities when compared to single parameter classifications employed in previous studies. Maximum performances are reached when combining the minimum/maximum ratio of the absorption coefficient and image variance. In this case, sensitivities and specificities over 0.9 can be achieved. These values are much higher than values obtained when only single parameter classifications were used, where sensitivities and specificities remained well below 0.8.