Brain tumour detection based on FFT, curve analysis, k-space and neural network classifier Online publication date: Thu, 10-Nov-2016
by V.K. Sheela; S. Suresh Babu
International Journal of Signal and Imaging Systems Engineering (IJSISE), Vol. 9, No. 6, 2016
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Signal and Imaging Systems Engineering (IJSISE):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook