Mass Sphering Approach for Three-Dimensional Reconstruction of Brain Tumor using MRI

R Preetha; S Vanaja; K Durga Devi; R Lathamanju; Nami Susan Kurian; Jacqulin Veda Jancy S

doi:10.17485/IJST/v15i3.1633

Article

Mass Sphering Approach for Three-Dimensional Reconstruction of Brain Tumor using MRI

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i3.1633

Year: 2022, Volume: 15, Issue: 3, Pages: 97-106

Original Article

Mass Sphering Approach for Three-Dimensional Reconstruction of Brain Tumor using MRI

R Preetha^1*, S Vanaja², K Durga Devi¹, R Lathamanju³, Nami Susan Kurian⁴, Jacqulin Veda Jancy S¹

¹Assistant Professor (SG), Department of Electronics and Communication Engineering, SRM Institute of Science & Technology
²Assistant Professor (SG), Department of Electronics and Communication Engineering,
Easwari Engineering College, Chennai
³Associate Professor, Department of Electronics and Communication Engineering, SRM
Institute of Science & Technology
⁴Assistant Professor, Department of Electronics and Communication Engineering,
Rajalakshmi Institute of Technology

*Corresponding Author
Email: [email protected]

Received Date:19 October 2021, Accepted Date:12 December 2021, Published Date:31 January 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: The objective of the research work is to reconstruct the brain tumor three-dimensionally with high degree of accuracy. Methods: This study describes 3D reconstruction of brain tumor using Mass Sphering Approach (MSA) algorithm. 39 weighted features are extracted from the non-tumor and tumor pixels. These weighted features are used to train the Support Vector Machine (SVM) algorithm. Number of training samples taken to train SVM algorithm are 268 and the testing sample are 64. The complete MR image set of a subject (64 axial slices) are detected for tumor pixels and these slices are concatenated to get volumetric tumor information. Findings: 5-step MSA algorithm is proposed which includes filtering, segmentation, classification, optimization and reconstruction. MR images are subjected to Rician noise which can be removed by a simple correction scheme, initiated to change the bias due to the Rician distribution of the noisy magnitude data. The filtered MR image slices are segmented and classified to detect the tumor areas and the tumor pixels are subjected for 3D reconstruction. The improvement in performance of MSA is depicted by comparing the algorithm with traditional SVM. Novelty: The accuracy achieved in detecting glioma and glioblastoma using MSA are 95.24% and 99% respectively which is highly remarkable.

Keywords: Glioma; voxel; Magnetic Resonance; Classification; Immune; Reconstruction

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Copyright

© 2022 Preetha et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)