Use of fractals in determining the malignancy degree of lung nodules

Noel Victor Amador-Legon, Marlen Perez-Diaz
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Abstract

A Computer-Assisted Detection (CAD) System for classification into malignant-benign classes using CT images is proposed.Two methods that use the fractal dimension (FD) as a measure of the lung nodule contour irregularities (Box counting and Power spectrum) were implemented. The LIDC-IDRI database was used for this study. Of these, 100 slices belonging to 100 patients were analyzed with both methods.The performance between both methods was similar with an accuracy higher than 90%. Little overlap was obtained between FD ranges for the different malignancy grades with both methods, being slightly better in Power spectrum. Box counting had one more false positive than Power spectrum.Both methods are able to establish a boundary between the high and low malignancy degree. To further validate these results and enhance the performance of the CAD system, additional studies will be necessary.
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利用分形确定肺结节的恶性程度
本研究提出了一种计算机辅助检测(CAD)系统,用于利用 CT 图像将肺结节分为恶性-良性两类。研究采用了两种方法(盒式计数法和功率谱法),利用分形维数(FD)来衡量肺结节轮廓的不规则性。这项研究使用了 LIDC-IDRI 数据库。两种方法的性能相似,准确率均高于 90%。两种方法在不同恶性程度的 FD 范围之间几乎没有重叠,在功率谱方面略胜一筹。两种方法都能在恶性程度高和恶性程度低之间建立界限。为了进一步验证这些结果并提高计算机辅助诊断系统的性能,有必要进行更多的研究。
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