Title: Brain tumour detection based on FFT, curve analysis, k-space and neural network classifier

Authors: V.K. Sheela; S. Suresh Babu

Addresses: Department of Computer Science, Noorul Islam University, Kumaracoil, Kanyakumari District, Thuckalay 629180, Tamil Nadu, India ' Electronics & Communication Engineering Department, TKM College of Engineering, Kollam 691005, Kerala, India

Abstract: Magnetic Resonance Imaging (MRI) has become an efficient instrument for clinical diagnoses in recent years. In this paper, an efficient MRI image segmentation for tumour detection is proposed using FFT, curve analysis and k-space. Input MRI image is pre-processed and segmentation is carried out using EM. Subsequently, features are extracted by using FFT, curve analysis and k-space. Finally, neural network classifier is employed to diagnose brain tumour. The MRI image dataset used to evaluate the proposed image technique is taken from the publicly available sources. The evaluation metrics used to evaluate the proposed technique consists of sensitivity, specificity and accuracy. Overall, the proposed technique could achieve sensitivity, specificity and accuracy values of 0.80, 0.81 and 0.805 respectively. The comparative analysis is also made comparing with other existing techniques. From the results, it can be seen that our proposed technique performed well and obtained better evaluation metrics than the existing methods.

Keywords: magnetic resonance imaging; MRI; image segmentation; brain tumours; tumour detection; classification; FFT; fast Fourier transform; k-space; curve analysis; EM algorithm; expectation maximization algorithm; neural networks.

DOI: 10.1504/IJSISE.2016.080273

International Journal of Signal and Imaging Systems Engineering, 2016 Vol.9 No.6, pp.393 - 402

Received: 06 Jul 2013
Accepted: 01 Jul 2014

Published online: 10 Nov 2016 *

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