Department of Physics, IIT Kanpur, India.
Biomed Phys Eng Express. 2020 Feb 18;6(2):025011. doi: 10.1088/2057-1976/ab6e17.
We report detection of cervical pre-cancer through their low coherence images by applying two dimensional multifractal detrended fluctuation analysis. Low coherent backscattered images of pre-cancerous cervical tissue sections were captured using a common path interferometric setup. The captured images contain both depth and lateral information of the spatial variation in refractive index (RI) occurring with progression of cervical pre-cancer. A two-dimensional multifractal detrended fluctuation analysis (2D MFDFA) was applied on these low coherent images to study the variations occurring in their fractal nature. Long-range correlations were observed in the RI fluctuations and the strength of multifractality was found to be stronger for higher grades of cervical pre-cancer. A combination of derived multifractal parameters, namely, the generalized Hurst exponent and width of singularity spectrum showed clear differences among the different grades of pre-cancers. Normal, CIN-I and CIN-II were clearly discriminated by application of support vector machine (SVM) using radial Bessel function (RBF) kernel. The specificities and sensitivities between normal and CIN-I, CIN-I and CIN-II and normal and CIN-II were found to be 94%, 88% and 93%, 96% and 98%, 100% respectively.
我们通过应用二维多重分形去趋势波动分析，从低相干图像中检测出宫颈癌前病变。使用共光路干涉仪装置捕获癌前宫颈组织切片的低相干背散射图像。所捕获的图像包含随着宫颈癌前病变进展而出现的折射率（RI）空间变化的深度和横向信息。对这些低相干图像应用二维多重分形去趋势波动分析（2D MFDFA），以研究其分形性质的变化。在 RI 波动中观察到长程相关性，并且发现宫颈癌前病变程度越高，多重分形性越强。衍生的多重分形参数（即广义赫斯特指数和奇异谱宽度）的组合表明，不同等级的宫颈癌前病变之间存在明显差异。通过使用径向贝塞尔函数（RBF）核的支持向量机（SVM）应用，正常、CIN-I 和 CIN-II 可以清楚地区分。正常与 CIN-I、CIN-I 与 CIN-II 以及正常与 CIN-II 之间的特异性和敏感性分别为 94%、88%和 93%、96%和 98%、100%。
