Fusion of brain MRI and CT images and Classification of brain tumor using Machine Learning Algorithm
Jayasheela M1, Gomathi E2
1Jayasheela, Professor in the Depart of Electronics and Communication Engineering at KIT-Kalaignar Karunanidhi Institute of Technology.
2Gomathi, Associate Professor in the Department of Electronics and Communication Engineering at KIT-Kalaignar Karunanidhi Institute of Technology.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 1699-1703 | Volume-8 Issue-4, November 2019. | Retrieval Number: C5703098319/2019©BEIESP | DOI: 10.35940/ijrte.C5703.118419
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Brain tumor is the most common and destructive disease which reduces the life time of people. The earlier detection of brain tumor plays a most important role for better treatment of the patient. In this paper, a new technique for brain tumor classification using machine learning by fusion of MRI and CT images are proposed. Image fusion is a process of fusing two or more images (i.e. MRI and CT scan images) to obtain a new one which contains more accurate information of the brain than any of the individual source images. Initially fusion of MRI and CT scan images has been carried out using Stationary Wavelet Transform (SWT). Then watershed transform is applied for image segmentation and discriminative robust local binary patter (DRLBP)is employed to extract the features exactly from the fused image. Classification of the tumor is done by Support Vector Machine (SVM) thereby reducing the generalization error and increasing the accuracy. The ultimate goal is to classify the tissues into normal and abnormal using machine learning algorithms .Image fusion process yields more accurate information of the brain than any of the individual source images.
Keywords: Image Fusion, SWT, Image Segmentation, DRLBP, GLCM, Tumor Classification.
Scope of the Article: Machine Learning.