Quantification of heterogeneity observed in medical images
Quantification of heterogeneity observed in medical images
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DOI：
10.1186/1471-2342-13-7
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发表时间：
2013-03-02
影响因子：
2.7
通讯作者：
Grigsby, Perry W.
中科院分区：
文献类型：
--
作者：
Brooks, Frank J.;Grigsby, Perry W.
背景资料：最近，人们对功能性灰度医学图像（例如通过磁共振或正电子发射断层扫描获得的图像）内的视觉上明显的异质性的量化产生了很大兴趣。在癌性肿瘤图像的情况下，灰度强度的变化意味着关键肿瘤生物学的变化。尽管有这些相当大的临床意义，目前还没有标准化的方法来测量通过这些成像models.Methods观察到的异质性：在这项工作中，我们激励和推导出一个统计测量图像的异质性。该统计量测量距离相关的平均偏差，从最平滑的强度等级可行。我们展示了如何使用此统计数据来自动对体内人类肿瘤的图像进行排序，以增加异质性。我们测试这种方法对目前的做法，排名通过专家visualinspection.Results的图像：我们发现，这种统计提供了一种手段的异质性量化超出了传统上用于相同目的的其他统计。我们证明了我们的排名方法后，肿瘤形状的影响，并发现该方法适用于各种各样的临床相关的肿瘤图像。我们发现，自动异质性排名同意非常密切与那些执行直观的experts.Conclusions：这些结果表明，我们的自动化方法可以可靠地用于排名，以增加异质性，肿瘤图像是否对象形状被认为是有助于异质性。自动异质性排名产生的客观结果比视觉排名更一致。减少图像解释的变异性将使更多的研究人员能够更好地研究观察到的肿瘤异质性的潜在临床意义。
Background: There has been much recent interest in the quantification of visually evident heterogeneity within functional grayscale medical images, such as those obtained via magnetic resonance or positron emission tomography. In the case of images of cancerous tumors, variations in grayscale intensity imply variations in crucial tumor biology. Despite these considerable clinical implications, there is as yet no standardized method for measuring the heterogeneity observed via these imaging modalities.Methods: In this work, we motivate and derive a statistical measure of image heterogeneity. This statistic measures the distance-dependent average deviation from the smoothest intensity gradation feasible. We show how this statistic may be used to automatically rank images of in vivo human tumors in order of increasing heterogeneity. We test this method against the current practice of ranking images via expert visual inspection.Results: We find that this statistic provides a means of heterogeneity quantification beyond that given by other statistics traditionally used for the same purpose. We demonstrate the effect of tumor shape upon our ranking method and find the method applicable to a wide variety of clinically relevant tumor images. We find that the automated heterogeneity rankings agree very closely with those performed visually by experts.Conclusions: These results indicate that our automated method may be used reliably to rank, in order of increasing heterogeneity, tumor images whether or not object shape is considered to contribute to that heterogeneity. Automated heterogeneity ranking yields objective results which are more consistent than visual rankings. Reducing variability in image interpretation will enable more researchers to better study potential clinical implications of observed tumor heterogeneity.