Affordable Access

Access to the full text

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

Authors
  • Fabelo, H.
  • Ortega, S.
  • Ravi, D.
  • Kiran, B. R.
  • Sosa, C.
  • Bulters, D.
  • Callico, G. M.
  • Bulstrode, H.
  • Szolna, A.
  • Pineiro, J. F.
  • Kabwama, S.
  • Madronal, D.
  • Lazcano, R.
  • OShanahan, A. J.
  • Bisshopp, S.
  • Hernandez, M.
  • Baez-Quevedo, A.
  • Yang, G. Z.
  • Stanciulescu, B.
  • Salvador, R.
  • And 2 more
Type
Preprint
Publication Date
Feb 11, 2024
Submission Date
Feb 11, 2024
Identifiers
DOI: 10.1371/journal.pone.0193721
Source
arXiv
License
Green
External links

Abstract

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a noncontact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

Report this publication

Statistics

Seen <100 times