Journal Article

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

Piñeiro, Juan F., J-O’Shanahan, Aruma, Fabelo, Himar, Kabwama, Silvester, Bisshopp, Sara, Yang, Guang-Zhong, Hernández, María, Callicó, Gustavo M., Juárez, Eduardo, Sosa, Coralia, Madroñal, Daniel, Báez, Abelardo, Stanciulescu, Bogdan, Salvador, Rubén, Bulstrode, Harry, Ravi, Daniele, Kiran, B. Ravi, Bulters, Diederik, Ortega, Samuel, Lazcano, Raquel, Szolna, Adam, Sarmiento, Roberto

2018

Overview

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 non-contact, 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. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. 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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Algorithms / Bioengineering / Biology and Life Sciences / Boundaries / Brain / Brain cancer / Brain tumors / Cancer / Classification / Clustering / Computer and Information Sciences / Computer Science / Computer Vision and Pattern Recognition / Embedding / Entropy / Filtration / Glioblastoma / Hospitals / Human health and pathology / Hyperspectral imaging / Image classification / Image detection / Image Processing / Image segmentation / Imaging / Infiltration / International conferences / Learning algorithms / Life Sciences / Machine learning / Medical diagnosis / Medical Imaging / Medicine and Health Sciences / Metastases / Mines / Multimedia / Neuroimaging / Neurosurgery / Patients / Physical Sciences / Research and Analysis Methods / Spectral classification / Stochasticity / Surgery / Survival / Tumors