Classification of Lung Nodules Using CT Images Based on Texture Features and Fractal Dimension Transformation
- Authors: Kravchenko V.F.1,2,3, Ponomaryov V.I.4, Pustovoit V.I.2, Rendon-Gonzalez E.4
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Affiliations:
- Kotelnikov Institute of Radio Engineering and Electronics, Russian Academy of Sciences
- Scientific and Technological Center of Unique Instrumentation, Russian Academy of Sciences
- Bauman Moscow State Technical University
- Instituto Politecnico Nacional de Mexico
- Issue: Vol 99, No 2 (2019)
- Pages: 235-239
- Section: Computer Science
- URL: https://journals.rcsi.science/1064-5624/article/view/225666
- DOI: https://doi.org/10.1134/S1064562419020297
- ID: 225666
Cite item
Abstract
A new computer-aided detection (CAD) system for lung nodule detection and selection in computed tomography scans is substantiated and implemented. The method consists of the following stages: preprocessing based on threshold and morphological filtration, the formation of suspicious regions of interest using a priori information, the detection of lung nodules by applying the fractal dimension transformation, the computation of informative texture features for identified lung nodules, and their classification by applying the SVM and AdaBoost algorithms. A physical interpretation of the proposed CAD system is given, and its block diagram is constructed. The simulation results based on the proposed CAD method demonstrate advantages of the new approach in terms of standard criteria, such as sensitivity and the false-positive rate.
About the authors
V. F. Kravchenko
Kotelnikov Institute of Radio Engineering and Electronics, Russian Academy of Sciences; Scientific and Technological Center of Unique Instrumentation, Russian Academy of Sciences; Bauman Moscow State Technical University
Author for correspondence.
Email: kvf-ok@mail.ru
Russian Federation, Moscow, 125009; Moscow, 117342; Moscow, 105005
V. I. Ponomaryov
Instituto Politecnico Nacional de Mexico
Author for correspondence.
Email: vponomar@ipn.mx
Mexico, Mexico, 04430
V. I. Pustovoit
Scientific and Technological Center of Unique Instrumentation, Russian Academy of Sciences
Email: vponomar@ipn.mx
Russian Federation, Moscow, 117342
E. Rendon-Gonzalez
Instituto Politecnico Nacional de Mexico
Email: vponomar@ipn.mx
Mexico, Mexico, 04430