<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abdelghani Tafsat</style></author><author><style face="normal" font="default" size="100%">Mohamed Laid Hadjili</style></author><author><style face="normal" font="default" size="100%">Ayache Bouakaz</style></author><author><style face="normal" font="default" size="100%">Nabil Benoudjit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised cluster-based method for segmenting biological tumor volume of laryngeal tumors in&lt;sup&gt; 18&lt;/sup&gt;F-FDG-PET images</style></title><secondary-title><style face="normal" font="default" size="100%">IET Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.1024</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">389-396</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In radiotherapy using 18-fluorodeoxyglucose positron emission tomography (&lt;sup&gt;18&lt;/sup&gt;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&amp;nbsp;&lt;sup&gt;18&lt;/sup&gt;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.</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue></record></records></xml>