TY - GEN
T1 - Classification of brain tumor MRIs using a kernel support vector machine
AU - Abd-Ellah, Mahmoud Khaled
AU - Awad, Ali Ismail
AU - Khalaf, Ashraf A.M.
AU - Hamed, Hesham F.A.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - The use of medical images has been continuously increasing, which makes manual investigations of every image a difficult task. This study focuses on classifying brain magnetic resonance images (MRIs) as normal, where a brain tumor is absent, or as abnormal, where a brain tumor is present. A hybrid intelligent system for automatic brain tumor detection and MRI classification is proposed. This system assists radiologists in interpreting the MRIs, improves the brain tumor diagnostic accuracy, and directs the focus toward the abnormal images only. The proposed computer-aided diagnosis (CAD) system consists of five steps: MRI preprocessing to remove the background noise, image segmentation by combining Otsu binarization and K-means clustering, feature extraction using the discrete wavelet transform (DWT) approach, and dimensionality reduction of the features by applying the principal component analysis (PCA) method. The major features were submitted to a kernel support vector machine (KSVM) for performing the MRI classification. The performance evaluation of the proposed system measured a maximum classification accuracy of 100% using an available MRIs database. The processing time for all processes was recorded as 1.23 seconds. The obtained results have demonstrated the superiority of the proposed system.
AB - The use of medical images has been continuously increasing, which makes manual investigations of every image a difficult task. This study focuses on classifying brain magnetic resonance images (MRIs) as normal, where a brain tumor is absent, or as abnormal, where a brain tumor is present. A hybrid intelligent system for automatic brain tumor detection and MRI classification is proposed. This system assists radiologists in interpreting the MRIs, improves the brain tumor diagnostic accuracy, and directs the focus toward the abnormal images only. The proposed computer-aided diagnosis (CAD) system consists of five steps: MRI preprocessing to remove the background noise, image segmentation by combining Otsu binarization and K-means clustering, feature extraction using the discrete wavelet transform (DWT) approach, and dimensionality reduction of the features by applying the principal component analysis (PCA) method. The major features were submitted to a kernel support vector machine (KSVM) for performing the MRI classification. The performance evaluation of the proposed system measured a maximum classification accuracy of 100% using an available MRIs database. The processing time for all processes was recorded as 1.23 seconds. The obtained results have demonstrated the superiority of the proposed system.
KW - Brain tumor
KW - DWT
KW - K-means
KW - KSVM
KW - MRIs classification
KW - PCA
UR - http://www.scopus.com/inward/record.url?scp=84988509430&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988509430&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-44672-1_13
DO - 10.1007/978-3-319-44672-1_13
M3 - Conference contribution
AN - SCOPUS:84988509430
SN - 9783319446714
T3 - Communications in Computer and Information Science
SP - 151
EP - 160
BT - Building Sustainable Health Ecosystems - 6th International Conference on Well-Being in the Information Society, WIS 2016, Proceedings
A2 - Wickramasinghe, Nilmini
A2 - Widén, Gunilla
A2 - Zhan, Ming
A2 - Nykänen, Pirkko
A2 - Li, Hongxiu
A2 - Suomi, Reima
PB - Springer Verlag
T2 - 6th International Conference on Well-Being in the Information Society, WIS 2016
Y2 - 16 September 2016 through 18 September 2016
ER -