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Histological Images of Malignant Breast Tumor: Mono and Multifractal Analysis

机译:恶性乳腺肿瘤的组织学图像:单和多重分形分析

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Current breast cancer risk prognosis methods have high prognostic variability which affects the chemotherapy decisions. Image analysis is a structure analysis tool that aids existing risk prognosis methods in order to improve quality of the prognosis. Fractal image analysis has been rarely used on breast tumor histology images for prognostic purposes and this paper deals with one such study using monofractal and multifractal analysis. Invasive breast tumor histology samples were used based on the absence of any systemic treatment. Obtained images were divided into two groups, named high and low risk, based on the risk prognosis for survival. Images were further subjected to computational analysis using binary and outline fractal dimensions, lacunarity for monofratal analysis and generalized dimension for multifractal analysis. Binary and outline fractal dimensions, as well as generalized dimension yielded statistically significant distinction between high risk and low risk groups. Lacunarity was also different but not statistically significant.
机译:当前的乳腺癌风险预后方法具有高的预后变异性,其影响化学疗法的决策。图像分析是一种结构分析工具,可帮助现有的风险预后方法以提高预后的质量。分形图像分析很少用于预后的乳腺肿瘤组织学图像,本文使用单分形和多分形分析进行了这样的研究。基于不进行任何全身治疗的情况,使用侵袭性乳腺肿瘤组织学样品。根据生存的风险预后,将获得的图像分为高风险和低风险两类。使用二进制和轮廓分形维数,对图像进行进一步的计算分析,对单帧分析使用盲点,对多分形分析使用广义维。二进制和轮廓分形维数以及广义维数在高风险组和低风险组之间产生了统计学上的显着区别。腔隙性也有所不同,但无统计学意义。

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