Department of Medical Biophysics, University of Toronto, 610 University Ave., Toronto, Ontario M5G 2M9, Canada.
Opt Lett. 2013 Apr 15;38(8):1280-2. doi: 10.1364/OL.38.001280.
We demonstrate a method for differentiating tissue disease states using the intrinsic texture properties of speckle in optical coherence tomography (OCT) images of normal and tumor tissues obtained in vivo. This approach fits a gamma distribution function to the nonlog-compressed OCT image intensities, thus allowing differentiation of normal and tumor tissues in an ME-180 human cervical cancer mouse xenograft model. Quantitative speckle intensity distribution analysis thus shows promise for identifying tissue pathologies, with potential for early cancer detection in vivo.
我们展示了一种使用光学相干断层扫描（OCT）图像中斑点的固有纹理特性来区分组织疾病状态的方法，这些图像是在体内获得的正常和肿瘤组织的 OCT 图像。该方法对非对数压缩的 OCT 图像强度拟合伽马分布函数，从而可以区分 ME-180 人宫颈癌异种移植模型中的正常组织和肿瘤组织。因此，定量斑点强度分布分析有望用于识别组织病理学，有可能实现体内早期癌症检测。
