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Florimbi G, Fabelo H, Torti E, Lazcano R, Madroñal D, Ortega S, Salvador R, Leporati F, Danese G, Báez-Quevedo A, Callicó GM, Juárez E, Sanz C, Sarmiento R. Accelerating the K-Nearest Neighbors Filtering Algorithm to Optimize the Real-Time Classification of Human Brain Tumor in Hyperspectral Images. Sensors (Basel) 2018; 18:s18072314. [PMID: 30018216 PMCID: PMC6068477 DOI: 10.3390/s18072314] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 07/12/2018] [Accepted: 07/15/2018] [Indexed: 11/19/2022]
Abstract
The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest Neighbors (KNN) filtering algorithm. The main goal of this study is to optimize and parallelize the KNN algorithm by exploiting the GPU technology to obtain real-time processing during brain cancer surgical procedures. This parallel version of the KNN performs the neighbor filtering of a classification map (obtained from a supervised classifier), evaluating the different classes simultaneously. The undertaken optimizations and the computational capabilities of the GPU device throw a speedup up to 66.18× when compared to a sequential implementation.
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Affiliation(s)
- Giordana Florimbi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.
| | - Himar Fabelo
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain.
| | - Emanuele Torti
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.
| | - Raquel Lazcano
- Centre of Software Technologies and Multimedia Systems (CITSEM), Technical University of Madrid (UPM), 28031 Madrid, Spain.
| | - Daniel Madroñal
- Centre of Software Technologies and Multimedia Systems (CITSEM), Technical University of Madrid (UPM), 28031 Madrid, Spain.
| | - Samuel Ortega
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain.
| | - Ruben Salvador
- Centre of Software Technologies and Multimedia Systems (CITSEM), Technical University of Madrid (UPM), 28031 Madrid, Spain.
| | - Francesco Leporati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.
| | - Giovanni Danese
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.
| | - Abelardo Báez-Quevedo
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain.
| | - Gustavo M Callicó
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain.
| | - Eduardo Juárez
- Centre of Software Technologies and Multimedia Systems (CITSEM), Technical University of Madrid (UPM), 28031 Madrid, Spain.
| | - César Sanz
- Centre of Software Technologies and Multimedia Systems (CITSEM), Technical University of Madrid (UPM), 28031 Madrid, Spain.
| | - Roberto Sarmiento
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain.
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Fabelo H, Ortega S, Ravi D, Kiran BR, Sosa C, Bulters D, Callicó GM, Bulstrode H, Szolna A, Piñeiro JF, Kabwama S, Madroñal D, Lazcano R, J-O’Shanahan A, Bisshopp S, Hernández M, Báez A, Yang GZ, Stanciulescu B, Salvador R, Juárez E, Sarmiento R. Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations. PLoS One 2018; 13:e0193721. [PMID: 29554126 PMCID: PMC5858847 DOI: 10.1371/journal.pone.0193721] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 02/06/2018] [Indexed: 11/18/2022] Open
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 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.
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Affiliation(s)
- Himar Fabelo
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
- * E-mail:
| | - Samuel Ortega
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
| | - Daniele Ravi
- The Hamlyn Centre, Imperial College London (ICL), London, United Kingdom
| | - B. Ravi Kiran
- Laboratoire CRISTAL, Université Lille 3, Villeneuve-d’Ascq, France
| | - Coralia Sosa
- Department of Neurosurgery, University Hospital Doctor Negrin, Las Palmas de Gran Canaria, Spain
| | - Diederik Bulters
- Wessex Neurological Centre, University Hospital Southampton, Tremona Road, Southampton, United Kingdom
| | - Gustavo M. Callicó
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
| | - Harry Bulstrode
- Department of Neurosurgery, Addenbrookes Hospital, University of Cambridge, Cambridge, United Kingdom
| | - Adam Szolna
- Department of Neurosurgery, University Hospital Doctor Negrin, Las Palmas de Gran Canaria, Spain
| | - Juan F. Piñeiro
- Department of Neurosurgery, University Hospital Doctor Negrin, Las Palmas de Gran Canaria, Spain
| | - Silvester Kabwama
- Wessex Neurological Centre, University Hospital Southampton, Tremona Road, Southampton, United Kingdom
| | - Daniel Madroñal
- Centre of Software Technologies and Multimedia Systems (CITSEM), Universidad Politecnica de Madrid (UPM), Madrid, Spain
| | - Raquel Lazcano
- Centre of Software Technologies and Multimedia Systems (CITSEM), Universidad Politecnica de Madrid (UPM), Madrid, Spain
| | - Aruma J-O’Shanahan
- Department of Neurosurgery, University Hospital Doctor Negrin, Las Palmas de Gran Canaria, Spain
| | - Sara Bisshopp
- Department of Neurosurgery, University Hospital Doctor Negrin, Las Palmas de Gran Canaria, Spain
| | - María Hernández
- Department of Neurosurgery, University Hospital Doctor Negrin, Las Palmas de Gran Canaria, Spain
| | - Abelardo Báez
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
| | - Guang-Zhong Yang
- The Hamlyn Centre, Imperial College London (ICL), London, United Kingdom
| | - Bogdan Stanciulescu
- Ecole Nationale Supérieure des Mines de Paris (ENSMP), MINES ParisTech, Paris, France
| | - Rubén Salvador
- Centre of Software Technologies and Multimedia Systems (CITSEM), Universidad Politecnica de Madrid (UPM), Madrid, Spain
| | - Eduardo Juárez
- Centre of Software Technologies and Multimedia Systems (CITSEM), Universidad Politecnica de Madrid (UPM), Madrid, Spain
| | - Roberto Sarmiento
- Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
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