Unsupervised cluster‐based method for segmenting biological tumour volume of laryngeal tumours in 18F‐FDG‐PET images. Issue 6 (6th April 2017)

• Record Type: Journal Article
• Authors: Tafsast, Abdelghani; Hadjili, Mohamed Laid; Bouakaz, Ayache; Benoudjit, Nabil
• Is Part Of: IET image processing. Volume 11:Issue 6(2017)
• Page Start: 389
• Page End: 396
• Publication Date: 2017-04-06
• Subjects: pattern clustering -- image segmentation -- tumours -- positron emission tomography -- medical image processing; unsupervised cluster‐based method -- laryngeal tumour biological tumour volume segmentation -- 18F‐FDG‐PET image -- 18‐fluorodeoxyglucose positron emission tomography -- radiotherapy -- biological tumour volume delineation -- BTV delineation -- k‐means clustering algorithm -- automatic optimal cluster number estimation -- intrinsic positron emission tomography image information -- patient clinical dataset; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.1024
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : In radiotherapy using 18‐fluorodeoxyglucose positron emission tomography ( 18 F‐FDG‐PET), the accurate delineation of the biological tumour volume (BTV) is a crucial step. In this study, the authors suggest a new approach to segment the BTV in 18 F‐FDG‐PET images. The technique is based on the k‐means clustering algorithm incorporating automatic optimal cluster number estimation, using intrinsic positron emission tomography image information. Clinical dataset of seven patients have a laryngeal tumour with the actual BTV defined by histology serves as a reference, were included in this study for the evaluation of results. Promising results obtained by the proposed approach with a mean error equal to (0.7%) compared with other existing methods in clinical routine, including fuzzy c‐means with (35.58%), gradient‐based method with (19.14%) and threshold‐based methods.