Automatic segmentation of nasopharyngeal carcinoma from CT images

Bilel Daoud, Ali Khalfallah, Leila Farhat, Wafa Mnejja, Ken'ichi Morooka, Med Salim Bouhlel, Jamel Daoud

研究成果: Contribution to journalArticle査読

抄録

The nasopharyngeal carcinoma (NPC) called also Cavum cancer becomes a public health problem for the Maghreb countries and Southeast Asia. The detection of this cancer could be carried out from computed tomography (CT) scans. In this context, we proposed two approaches based on image clustering to locate affected tissues by the Cavum cancer. These approaches are based respectively on E-M and Otsu segmentation. Compared to the physician manual contouring, our automatic detection proves that the detection of the cancer while using the Otsu clustering in terms of precision, recall and F-measure is more efficient than E-M. Then, we merged the results of these two methods by using the AND and the OR logical operators. The AND fusion yields to an increase of the precision while the OR fusion raises the recall. However, the detection of the NPC using Otsu remain the best solution in terms of F-Measure. Compared to previous studies that provide a surface analysis of the NPC, our approach provides a 3D estimation of this tumour ensuring a better analysis of the patient folder.

本文言語英語
ページ(範囲)240-257
ページ数18
ジャーナルInternational Journal of Biomedical Engineering and Technology
33
3
DOI
出版ステータス出版済み - 2020

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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