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
Highlights
Brain tumor detection is the most challenging task as the boundary of tumor is not confined and it may grow abnormally and cause damage to adjacent tissues(1)
This study presents a new approach, called Mass Sphering Approach (MSA) to deal with 3d brain tumor visualization
A 3D image reconstruction method is proposed based on MSA and is implemented for brain tissue reconstruction
Summary
Brain tumor detection is the most challenging task as the boundary of tumor is not confined and it may grow abnormally and cause damage to adjacent tissues(1). A report from United News of India (UNI) reveals that, per year 28,142 new brain tumor cases. A survey is made to derive the characteristics of brain tumor and segmentation or classification techniques for successful detection of brain tumor(3). A hybrid system of integrated K-means clustering and Fuzzy C means model is developed for segmentation of tumor. The performance of this approach deals with minimizing the executing time with expected accuracy. A new method called Quantum Artificial Immune System - deep spiking neural network (QAIS-DSNN) was proposed to segment and distinguish brain tumors from MR images (5)
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