DI-UMONS : Dépôt institutionnel de l’université de Mons

Recherche transversale
(titres de publication, de périodique et noms de colloque inclus)
2018-04-25 - Article/Dans un journal avec peer-review - Anglais - 14 page(s)

Larhmam Mohamed , Mahmoudi Said , Drisis Stylianos, Benjelloun Mohammed , "A Texture Analysis Approach for Spine Metastasis Classification in T1 and T2 MRI" in Bioinformatics and Biomedical Engineering, 2, 2018, 198-211, 10.1007/978-3-319-78759-6_19

  • Codes CREF : Techniques d'imagerie et traitement d'images (DI2770), Intelligence artificielle (DI1180), Imagerie médicale, radiologie, tomographie (DI3243)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech)

Abstract(s) :

(Anglais) This paper presents a learning based approach for the classification of pathological vertebrae. The proposed method is applied to spine metastasis, a malignant tumor that develops inside bones and requires a rapid diagnosis for an effective treatment monitoring. We used multiple texture analysis techniques to extract useful features from two co-registered MR images sequences (T1, T2). These MRIs are part of a diagnostic protocol for vertebral metastases follow up. We adopted a slice by slice MRI analysis of 153 vertebra region of interest. Our method achieved a classification accuracy of 90.17%±5.49 , using only a subset of 67 relevant selected features from the initial 142.

Mots-clés :
  • (Anglais) MRI
  • (Anglais) Texture analysis
  • (Anglais) Machine Learning