Fusion-Based Segmentation Technique for Improving the Diagnosis of MRI Brain Tumor in CAD Applications
Diagnosing the brain tumor from Magnetic Resonance Imaging (MRI) in Computer-Aided Diagnosis (CAD) applications is one of the challenging task in medical image processing. Traditionally many segmentation methods are used to address this issue. This paper introduces a segmentation method along with image fusion. Here a Discrete Wavelet Transform (DWT) method is chosen, for image fusion followed by segmentation using Support Vector Machine (SVM) for detecting the abnormality region. The types of MRI images considered here include T1-weighted (T1-w), T2-weighted (T2-w) and FLAIR images. The various fusion combinations are T1-w and T2-w, T1-w and FLAIR, T2-w and FLAIR. Experimental results suggest that on an average, fusion-based segmented result is superior to non-fusion-based segmented result.
KeywordsFLAIR DWT Image fusion MRI SVM Segmentation T1-w T2-w
The website link for BRATS image database is https://www.smir.ch/BRATS/Start2013. This data set was supported for my doctoral degree purpose only. We have no conflict of interest with regard to the work presented. Ethical approval to conduct this study was obtained for my research work. Informed consent was obtained from all individual participants in the study.
- 1.Abdullah N, Chuen L, Ngah U, Ahmad K (2011) Improvement of MRI brain classification using principal component analysis. In: 2011 IEEE international conference on control system, computing and engineering (ICCSCE). IEEE, pp 557–561Google Scholar
- 2.Najafi S, Amirani M, Sedghi Z (2011) A new approach to MRI brain images classification. In: 2011 19th Iranian conference on electrical engineering (ICEE). IEEE, pp 1–5Google Scholar
- 3.Singh D, Kaur K. Classification of abnormalities in brain MRI images using GLCM, PCA and SVM. Int J Eng I:243–248 (Online). http://www.ijeat.org/attachments/File/v1i6/F0676081612.pdf2012
- 6.Ng CR, Than JCM, Noor NM, Rijal OM (2015) Double segmentation method for brain region using FCM and graph cut for CT scan imges. In: IEEE international conference on signal and image processing applications, 978-1-4799-8996-6/15Google Scholar
- 8.Barman PC, Miah MS, Singh BC, Khatun MT (2011) MRI image segmentation using level set method and implement an medical diagnosis system. Comput Sci Eng Int J 1(5)Google Scholar
- 9.Liu J, Guo L (2015) A new brain MRI image segmentation strategy based on wavelet transform and K-means clustering. IEEE 978-1-4799-8920-1-15Google Scholar
- 10.Lan T, Xiao Z, Li Y, Ding Y, Qin Z (2014) Multimodal medical image fusion using wavelet transform and human vision system. ICALIP,978-1-4799-3903-9/4. IEEEGoogle Scholar
- 11.Indira KP, Hemamalini R (2015) Impact of co-efficient selection rules on the performance of DWT based fusion on medical images. In: International conference on robotics, automation, control and embedded systems. ISBN 978-81-925974-3-0Google Scholar
- 12.Vijayakumar B, Chaturvedi A (2012) Automatic brain tumors segmentation of MR images using fluid vector flow and support vector machine. Res J Inf Technol 4:108–114Google Scholar
- 13.Hota HS, Shukla SP, Gulhare K (2013) Review of intelligent techniques applied for classification and preprocessing of medical image data. IJCSI Int J Comput Sci Issues 1:267–272 (Online). http://www.ijcsi.org/papers/IJCSI-10-1-3-267-272.pdf