AUTOMATIC LUNG NODULE DETECTION BASED ON STATISTICAL REGION MERGING AND SUPPORT VECTOR MACHINES

Authors

  • Elaheh Aghabalaei Khordehchi Iran University of Science and Technology
  • Ahmad Ayatollahi Iran University of Science and Technology
  • Mohammad Reza Daliri Iran University of Science and Technology

DOI:

https://doi.org/10.5566/ias.1679

Keywords:

CT X-ray images, morphological filtering, nodule detection, nonlinear partial differential quations, statistical region merging, support vector machine

Abstract

Lung cancer is one of the most common diseases in the world that can be treated if the lung nodules are detected in their early stages of growth. This study develops a new framework for computer-aided detection of pulmonary nodules thorough a fully-automatic analysis of Computed Tomography (CT) images. In the present work, the multi-layer CT data is fed into a pre-processing step that exploits an adaptive diffusion-based smoothing algorithm in which the parameters are automatically tuned using an adaptation technique. After multiple levels of morphological filtering, the Regions of Interest (ROIs) are extracted from the smoothed images. The Statistical Region Merging (SRM) algorithm is applied to the ROIs in order to segment each layer of the CT data. Extracted segments in consecutive layers are then analyzed in such a way that if they intersect at more than a predefined number of pixels, they are labeled with a similar index. The boundaries of the segments in adjacent layers which have the same indices are then connected together to form three-dimensional objects as the nodule candidates. After extracting four spectral, one morphological, and one textural feature from all candidates, they are finally classified into nodules and non-nodules using the Support Vector Machine (SVM) classifier. The proposed framework has been applied to two sets of lung CT images and its performance has been compared to that of nine other competing state-of-the-art methods. The considerable efficiency of the proposed approach has been proved quantitatively and validated by clinical experts as well.

Author Biographies

Elaheh Aghabalaei Khordehchi, Iran University of Science and Technology

Electrical Engineering Department

Ahmad Ayatollahi, Iran University of Science and Technology

Electrical Engineering Department

Mohammad Reza Daliri, Iran University of Science and Technology

Electrical Engineering Department

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Published

2017-06-23

How to Cite

Aghabalaei Khordehchi, E., Ayatollahi, A., & Daliri, M. R. (2017). AUTOMATIC LUNG NODULE DETECTION BASED ON STATISTICAL REGION MERGING AND SUPPORT VECTOR MACHINES. Image Analysis and Stereology, 36(2), 65–78. https://doi.org/10.5566/ias.1679

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Original Research Paper