Two-dimensional airway analysis using probabilistic neural networks

Jun Tan; Bin Zheng; Sang Cheol Park; Jiantao Pu; Frank C. Sciurba; Joseph K. Leader

doi:10.1117/12.844497

9 March 2010 Two-dimensional airway analysis using probabilistic neural networks

Jun Tan, Bin Zheng, Sang Cheol Park, Jiantao Pu, Frank C. Sciurba, Joseph K. Leader

Proceedings Volume 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging; 762612 (2010) https://doi.org/10.1117/12.844497
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

Although 3-D airway tree segmentation permits analysis of airway tree paths of practical lengths and facilitates visual inspection, our group developed and tested an automated computer scheme that was operated on individual 2-D CT images to detect airway sections and measure their morphometry and/or dimensions. The algorithm computes a set of airway features including airway lumen area (A_i), airway cross-sectional area (A_w), the ratio (R_a) of Ai to Aw, and the airway wall thickness (T_w) for each detected airway section depicted on the CT image slice. Thus, this 2-D based algorithm does not depend on the accuracy of 3-D airway tree segmentation and does not require that CT examination encompasses the entire lung or reconstructs contiguous images. However, one disadvantage of the 2-D image based schemes is the lack of the ability to identify the airway generation (G_b) of the detected airway section. In this study, we developed and tested a new approach that uses 2-D airway features to assign a generation number to an airway. We developed and tested two probabilistic neural networks (PNN) based on different sets of airway features computed by our 2-D based scheme. The PNNs were trained and tested on 12 lung CT examinations (8 training and 4 testing). The accuracy for the PNN that utilized Ai and Ra for identifying the generation of airway sections varies from 55.4% - 100%. The overall accuracy of the PNN for all detected airway sections that are spread over all generations is 76.7%. Interestingly, adding wall thickness feature (T_w) to PNN did not improve identification accuracy. This preliminary study demonstrates that a set of 2-D airway features may be used to identify the generation number of an airway with reasonable accuracy.

Citation Download Citation

Jun Tan, Bin Zheng, Sang Cheol Park, Jiantao Pu, Frank C. Sciurba, and Joseph K. Leader "Two-dimensional airway analysis using probabilistic neural networks", Proc. SPIE 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging, 762612 (9 March 2010); https://doi.org/10.1117/12.844497

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KEYWORDS

Computed tomography

Image segmentation

Lung

Neural networks

Chronic obstructive pulmonary disease

3D image processing

Evolutionary algorithms

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