Spatio-spectral classification of hyperspectral images for brain cancer
  detection during surgical operations
H. Fabelo S. Ortega D. Ravi B. R. Kiran C. Sosa D. Bulters G. M. Callico H. Bulstrode A. Szolna J. F. Pineiro S. Kabwama D. Madronal R. Lazcano A. J. OShanahan S. Bisshopp M. Hernandez A. Baez-Quevedo G. Z. Yang B. Stanciulescu R. Salvador E. Juarez R. Sarmiento
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.