MR Brain Tumor Classification and Segmentation Via Wavelets

Title

MR Brain Tumor Classification and Segmentation Via Wavelets

Creator

Menaka Devi T.; Ramani G.; Xavier Arockiaraj S.

Description

Timely, accurate detection of magnetic resonance (MR) images of brain is most important in the medical analysis. Many methods have already explained about the tumor classification in the literature. This paper explains the method of classifying MR brain images into normal or abnormal (affected by tumor), abnormality segments present in the image. This paper proposes DWT-discrete wavelet transform in first step to extract the image features from the given input image. To reduce the dimensions of the feature image principle component Analysis (PCA) is employed. Reduced extracted feature image is given to kernel support vector machine (KSVM) for processing. The data set has 90 brain MR images (both normal and abnormal) with seven common diseases. These images are used in KSVM process. Gaussian Radial Basis (GRB) kernel is used for the classification method proposed and yields maximum accuracy of 98% compared to linear kernel (LIN). From the analysis, compared with the existing methods GRB kernel method was effective. If this classification finds abnormal MR image with tumor then the corresponding part is separated and segmented by thresholding technique. 2018 IEEE.

Source

2018 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2018

Date

2018-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Discrete Wavelet Transform (DWT); Fejerkorovkin Filter; Kernel Support vector Machine (KSVM); Principlecomponent Analysis (PCA); Thresholding

Coverage

Menaka Devi T., Department of Electronics and Communication Engineering, Adhiyamann College of Engineering, Hosur, 635109, India; Ramani G., Department of Electronics and Communication Engineering, CHRIST, Faculty of Engineering, Bengaluru, 560074, India; Xavier Arockiaraj S., Department of Electronics and Communication Engineering, CHRIST, Faculty of Engineering, Bengaluru, 560074, India

Rights

Restricted Access

Relation

ISBN: 978-153863624-4

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/WiSPNET.2018.8538643

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059420027&doi=10.1109%2fWiSPNET.2018.8538643&partnerID=40&md5=2f7735aeefa4acd13b69b342756f1577