Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images
- PDF / 1,377,300 Bytes
- 10 Pages / 595.276 x 790.866 pts Page_size
- 94 Downloads / 189 Views
ORIGINAL RESEARCH
Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images S. Krishnakumar1 · K. Manivannan2 Received: 4 November 2019 / Accepted: 2 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract From the classifications, an effective brain tumor classification and segmentation is the curious part for identifying the tumor and non-tumor cells in brain and the cell levels are evaluated. The brain tumor segmentation and classification is established on their experiences. The accuracy of tumor segmentation is very crucial to diagnosis accuracy. So, in our work we are align and improve an approach for tumor identification applying brain MR image segmentation. With an efficient, accurate and reproducible manner, the aim of our suggested method is to evaluate the tumor. Then the brain tumor is separated by using the effective techniques. For segmentation process, first the MRI image must be preprocessed. Next, the process of feature extraction is done by using preprocessed images. In feature extraction process, a raised Gabor wavelet transform (IGWT) is applied. In this research, the means of optimization technique is changed from the traditional Gabor wavelet transform. And the effectiveness of that optimization technique is aligned by using an oppositional fruit fly algorithm. At the end of the process, feature values are transferred in to the clustering process for segmentation. In this article we are introduced an algorithm called as rough k means clustering algorithm for segmentation. Here, we are applying an oppositional fruit fly algorithm to develop an effectiveness of the Gabor filter. Further to raise the classification accuracy of brain tumor we are introduced a multi kernel support vector machine algorithm. Keywords MR image · Brain tumor · Rough k means clustering · Support vector machine · Segmentation
1 Introduction Image processing and its segmentation is the one of the interesting area of medical science. In medical image technology, both MRI and computerized tomography scan (CT) applied to develop the pictures of inside body that MRI renders accurate visualization of anatomical structures of tissues. When equate to CT scan, MRI is better since it is not affects the human body (Patel and Doshi 2014). Different types of cells are grouped to form a human body. Brain is a highly specialized and sensitive organ of human body. For human beings, brain tumor is a very dangerous disease (Borole et al. 2015). In the medical science, magnetic resonance imaging is a tool that can develop detail pictures of * S. Krishnakumar [email protected] 1
Department of ECE, Theni Kammvar Sangam College of Technology, Theni, India
Department of CSE, PSNA College of Engineering and Technology, Dindigul, India
2
parts of the body and also to inquire the brain tumor and its segmentation from image (Kaur and Rani 2016). For humans, the brain tumor is the disease, that cells are grown in the brain. Brain tumor
Data Loading...
