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Fractal dimensionality analysis of normal and cancerous mammary gland thermograms

机译:正常和癌性乳腺热像图的分形维数分析

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摘要

Thermography may enable early detection of a cancer tumour within a mammary gland at an early, treatable stage of the illness, but thermogram analysis methods must be developed to achieve this goal. This study analyses the feasibility of applying the Hurst exponent readings algorithm for evaluation of the high dimensionality fractals to reveal any possible difference between normal thermograms (NT) and malignant thermograms (MT). Thermograms were obtained using electronic contact thermography. Significant differences in the Hurst exponent readings for the MT and the NT were observed when comparing the following: The right NT (H = 0.40 ± 0.13) (mean ± standard deviation) and the right MT (H = 0.36 ± 0.10) (p = 0.037) (p - significance level).The left NT (H = 0.40 ± 0.14) and the left MT (H = 0.37 ± 0.11) (p = 0.035). Given these differences, we conclude that the Hurst exponent computed with the aforementioned algorithm may be used in thermogram processing for the early diagnosis of mammary gland cancer.
机译:热像仪可以在疾病的早期,可治疗的阶段早期发现乳腺内的癌症肿瘤,但是必须开发热分析图方法才能实现这一目标。这项研究分析了应用Hurst指数读数算法评估高维分形的可行性,以揭示正常温度图(NT)和恶性温度图(MT)之间的任何可能差异。使用电子接触热成像法获得热分析图。当比较以下各项时,观察到MT和NT的赫斯特指数读数存在显着差异:右NT(H = 0.40±0.13)(平均值±标准差)和右MT(H = 0.36±0.10)(p = 0.037)(p-显着性水平)。左NT(H = 0.40±0.14)和左MT(H = 0.37±0.11)(p = 0.035)。考虑到这些差异,我们得出的结论是,使用上述算法计算出的赫斯特指数可用于热成像图处理,以早期诊断乳腺癌。

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