人体甲状腺动态红外图像多重分形分析
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摘要
开展了健康对象甲状腺动态红外图像的多重分形特征研究,并对不同个体甲状腺多重分形特征参数进行了统计分析与差异性检验。首先,在恒温恒湿实验环境下,获取多帧人体甲状腺红外图像,并对其进行网格划分,形成温度时间序列。然后,探讨了适合人体甲状腺多重分形分析的原始信号长度、小波变换尺度因子、统计矩阶数的取值。在确定好上述参数后,对温度时间序列进行多尺度小波变换,求解其小波变换模极大,进而获取不同健康对象甲状腺左右叶多重分形特征参数的分布特性。研究结果表明:健康对象甲状腺多重分形特征谱线分形维数取得极值处对应的奇异性指数c_1的分布集中在1.1～1.3范围内,间隙系数c_2的分布则集中于0.002～0.005范围内,二者分布特征不存在个体差异的检验水准α=0.01;多重分形谱线半峰宽集中于0.164～0.166范围内且不存在个体差异的检验水准α=0.05。
The multifractal characteristics of thyroid dynamic infrared images of healthy subjects were studied in this paper, and the statistical analysis and differential test of thyroid multifractal characteristics of different individuals were performed. Firstly, in the constant temperature and humidity experiment environment, multiple frames of human thyroid infrared images were acquired and meshed to form a temperature-time series. Then, the original signal length, wavelet transformed scale factor and statistical moment order of the multi-fractal analysis of human thyroid were discussed. After determining the above parameters, the multi-scale wavelet transform of the temperature-time series was performed to solve the Wavelet Transform Modulus Maximal, and then the distribution characteristics of multi-fractal characteristics of the left and right thyroid lobe of different healthy subjects were solved.The results show that the singularity index c_1 of the thyroid multi-fractal characteristic line dimension of healthy subjects concentrates in the range of 1.1-1.3, and the gap coefficient c_2 concentrates in the range of 0.002-0.005. The test level of individual differences is α=0.01; the half-width of the multi-fractal line concentrates in the range of 0.164-0.166 and the test level of no individual difference α is0.05.
The multifractal characteristics of thyroid dynamic infrared images of healthy subjects were studied in this paper, and the statistical analysis and differential test of thyroid multifractal characteristics of different individuals were performed. Firstly, in the constant temperature and humidity experiment environment, multiple frames of human thyroid infrared images were acquired and meshed to form a temperature-time series. Then, the original signal length, wavelet transformed scale factor and statistical moment order of the multi-fractal analysis of human thyroid were discussed. After determining the above parameters, the multi-scale wavelet transform of the temperature-time series was performed to solve the Wavelet Transform Modulus Maximal, and then the distribution characteristics of multi-fractal characteristics of the left and right thyroid lobe of different healthy subjects were solved.The results show that the singularity index c_1 of the thyroid multi-fractal characteristic line dimension of healthy subjects concentrates in the range of 1.1-1.3, and the gap coefficient c_2 concentrates in the range of 0.002-0.005. The test level of individual differences is α=0.01; the half-width of the multi-fractal line concentrates in the range of 0.164-0.166 and the test level of no individual difference α is0.05.
引文
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[2]Das K,Sungh R,Mishras C.Numerical analysis for determination of the presence of a tumor and estimation of its size and location in a tissue[J].Journal of Thermal Biology,2013,38:32-40.
[3]Acharya U R,Ng E Y K,Tan J H.Thermography based breast cancer detecting using texture features and support vector machine[J].Journal of Medical Systems,2012,33:1503-1510.
[4]Jones B F,Plassmann P.Digital infrared thermal imaging of human skin[J].IEEE Engineering in Medicine and Biology Magazine,2002,21(6):41-48.
[5]Silva L F,Santos A A S M D,Bravo R S,et al.Hybrid analysis for indicating patients with breast cancer using temperature time series[J].Computer Methods and Programs in Biomedicine,2016,130:142-153.
[6]Gavriloaia G,Ghemigian A M,Gavriloaia M R.Infrared signature analysis of the thyroid tumors[C]//European Conference on Biomedical Optics.Optical Society of America,2009:7371_1F.
[7]Gerasimova E,Auditb,Roux S G,et al.Multifractal analysis of dynamic infrared imaging of breast cancer[J].EPL(Europhysics Letters),2014,104(6):68001.
[8]Muzy J F,Bacry E,Arenodo A.The multifractal formalism revisited with wavelets[J].International Journal of Bifurcation and Chaos,1994,4(2):245-302.
[9]Bacry E,Muzy J F,Arneodo A.Singularity spectrum of fractal signals from wavelet analysis:Exact results[J].Journal of Statistical Physics,1993,70(3-4):635-674.
[10]Arendod A,Bacry E,Muzy J F.The thermodynamics of fractals revisited with wavelets[J].Physica A:Statistical Mechanics and its Applications,1995,213(1-2):232-275.
[11]Ivanov P C,Amaral L A N,Goldberger A L,et al.Multifractality in human heartbeat dynamics[J].Nature,1999,399(6735):461-465.
[12]Ivanov P C,Nunes Amaral L A,Goleberger A L,et al.From 1/f noise to multifractal cascades in heartbeat dynamics[J].Chaos:An Interdisciplinary Journal of Nonlinear Science,2001,11(3):641-652.
[13]Goldberger A L,Amaral L A N,Hausdorff J M,et al.Fractal dynamics in physiology:alterations with disease and aging[J].Proceedings of the National Academy of Sciences,2002,99(S1):2466-2472.