HOME / PUBLICATIONS /

Publications by Author: Mohamed Laid Hadjili

2019
Tafsast A, Hadjili ML, Bouakaz A, Benoudjit N. Characterization of Melanoma Using Convolutional Neural Networks and Dermoscopic Images. International Conference on Electrical Engineering and Control Applications. 2019 :1147-1155.
2018
Tafsast A, Ferroudji K, Hadjili ML, Bouakaz A, Benoudjit N. Automatic microemboli characterization using convolutional neural networks and radio frequency signals. 2018 International Conference on Communications and Electrical Engineering (ICCEE). 2018 :1-4.
2017
Tafsast A, Hadjili ML, Bouakaz A, Benoudjit N. Unsupervised cluster-based method for segmenting biological tumour volume of laryngeal tumours in 18 F-FDG-PET images. IET Image Processing. 2017;11 (6) :389-396.
2016
Tafsat A, Hadjili ML, Bouakaz A, Benoudjit N. Unsupervised cluster-based method for segmenting biological tumor volume of laryngeal tumors in 18F-FDG-PET images. IET Image Processing. 2016;11 (6) :389-396.Abstract

In radiotherapy using 18-fluorodeoxyglucose positron emission tomography (18F-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 18F-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.