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Updated: Sep 29, 2026
Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
Published on: January 10, 2025
Fractal analysis of small peripheral pulmonary nodules in thin-section CT: evaluation of the lung-nodule interfaces
Shoji Kido1, Keiko Kuriyama, Masahiko Higashiyama
1Department of Computer Science and System Engineering, Faculty of Wngineering, Yamaguchi University, Japan. kido@ai.csse.yamaguchi-u.ac.jp
Objectives:
To analyze the lung-nodule interfaces on small peripheral pulmonary nodules (<2 cm) in thin-section CT (HRCT) images with fractal analysis.
Methods:
Thin-section CT images from 70 patients with bronchogenic carcinomas (61 adenocarcinomas and 9 squamous cell carcinomas) and 47 patients with benign pulmonary nodules (23 hamartomas, 13 organizing pneumonias, and 11 tuberculomas) were used. For calculation of fractal dimensions (FDs), the authors used a box-counting method for binary- and gray-scale images of nodules. FD(two-dimensional [2D]) was an FD obtained from the binary image, and FD(three-dimensional [3D]) was an FD obtained from the gray-scale image.
Results:
The FD(2D)s of hamartomas were smaller than those of other nodules ( < 0.05). The FD(3D)s obtained from the gray-scale images of organizing pneumonias and tuberculomas were greater than those of bronchogenic carcinomas ( < 0.0001) and hamartomas ( < 0.0001). In bronchogenic carcinomas, FD(3D)s of adenocarcinomas were greater than those of squamous cell carcinomas ( < 0.05).
Conclusions:
Fractal dimensions reflect the characteristics of the lung-nodule interfaces of small peripheral pulmonary nodules. The FD(2D)s revealed the irregularities of the contours. On the other hand, FD(3D)s revealed the complexities of the heterogeneous textures. With use of FD(2D) and FD(3D), it may be possible to distinguish bronchogenic carcinomas from benign pulmonary nodules. Moreover, FD(3D) may make it possible to distinguish between adenocarcinomas and squamous cell carcinomas.
