A hierarchical stochastic modelling approach for reconstructing lung tumour geometry from 2D CT images

A hierarchical stochastic modelling approach for reconstructing lung tumour geometry from 2D CT images
درجه علمی نشریه: 
دوره: 
۳۰
شماره: 
۶
شماره صفحه (از - تا): 
۹۷۳-۹۹۲
چکیده

Lung cancer is one of the deadliest cancers in both men and women.‎ Nowadays, several methods are used to cure this cancer including surgery and radiotherapy.‎ These methods require prior knowledge about the shape of tumours.‎ This type of knowledge may also help physicians to determine the cancer type.‎ In this paper we propose a novel approach for 3D reconstruction of tumour geometry from a sequence of 2D images.‎ The proposed approach consists of two phases: tumour segmentation from computed tomography (CT) images and 3D shape reconstruction.‎ Segmentation is conducted using snake optimization and Gustafson–Kessel clustering.‎ For 3D reconstruction, first, we propose a new approach to interpolate some intermediate slices between original slices.‎ Then, the well-known marching cubes algorithm is used for surface reconstruction.‎ Eventually, we smoothen the surface using an explicit fairing algorithm.‎ Experiments show that our new approach can highly improve the quality and the accuracy of the reconstructed tumour shape.‎

استناد: 

Afshar, Parnian, abbas Ahmadi, Azadeh Mohebi, and mohammad Hossein Fazel Zarandi.‎ 2018.‎ A hierarchical stochastic modelling approach for reconstructing lung tumour geometry from 2D CT images.‎ Journal of Experimental & Theoretical Artificial Intelligence, ۳۰ (۶۰): ۹۷۳-۹۹۲.

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