Abstract
Lung cancer is a common dangerous cancer among men and women
worldwide. Using the information about the 3D shape of the lung tumours is useful for
determining the cancer type and drug delivery problems. This chapter aims to propose
a novel approach for 3D tumour reconstruction from a sequence of 2D parallel CT
images. To achieve this goal, we first preprocessed CT images before implementing
DBSCAN clustering for lung segmentation. We defined efficient features that made the
results more accurate and improved the speed of the DBSCAN algorithm. Next, we
designed a deep autoencoder network to extract useful features from each cluster. Then
classifications methods are applied to classify tumours among the other clusters. By
extracting the tumour area from 2D images, we can construct the 3D shape of tumours
using the Marching Cubes algorithm. A novel stochastic approach is proposed to
interpolate some intermediate slices between available slices to improve the accuracy
of the ultimate 3D shape. Complexity and errors are reduced in the presented approach
compared to the previous methods. Finally, results indicate that our approach is more
automatic and accurate than the other 3D lung tumour modelling approaches.
Keywords: 3D shape reconstruction, Clustering, Deep autoencoder, Lung cancer, Stochastic modelling.
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