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

• Published in: Journal of experimental & theoretical artificial intelligence Vol. 30; no. 6; pp. 973 - 992
• Main Authors: Afshar, Parnian, Ahmadi, Abbas, Mohebi, Azadeh, Fazel Zarandi, M.H.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.11.2018; Taylor & Francis Ltd
• Subjects: Algorithms; Cancer; Clustering; Computed tomography; Image reconstruction; Image segmentation; Lung cancer; Marching cubes algorithms; Medical imaging; Physicians; Radiation therapy; stochastic modelling; Tumors; tumour recognition; Two dimensional models; volume reconstruction
• ISSN: 0952-813X; 1362-3079
• DOI: 10.1080/0952813X.2018.1509894

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 optimisation 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.