A Novel Approach for Identifying the Stages of Brain Tumor
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2014 by IJCTT Journal|
|Year of Publication : 2014|
|Authors : Y.V.Sri Varsha , S.Prayla Shyry|
|DOI : 10.14445/22312803/IJCTT-V10P116|
Y.V.Sri Varsha , S.Prayla Shyry."A Novel Approach for Identifying the Stages of Brain Tumor". International Journal of Computer Trends and Technology (IJCTT) V10(2):92-96, Apr 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
In recent years, brain tumor is one of the major cause for death in people. The most efficient way to reduce the brain tumor is to detect it at the earlier stage itself. Traditional systems use various image processing techniques to identify the brain tumor at the earlier stages. Among the multi modal images each one has their own importance. In the proposed system, neural network is used. The neural network is trained with selected features and then features are extracted and tumor affected regions can be detected. The future enhancement is to detect the stages of brain tumor for each Multimodal image in more efficient and short duration.
Kavitha.C,S.Sangeeta,”Automatic Multimodality Brain Tumor Detection, “International Journal of Emerging Technology and Advanced Engineering, volume 3, March 2013.
Dhanalakshmi&T.Kanimozhi,”Automated Segmentation of Brain Tumor using K-means clustering and its area calculation,” International Journal of Advanced Electrical and Electronics Engineering, Volume-2, 2013.
Meghana Nagori, Shivaji Mutule,praful Sonarkar,”Detection of Brain Tumor by mining FMRI images, “International Journal of advanced research in Computer and communication Engineering, volume 2,March 2013.
 Vivek Angoth, CYN Dwith, Amarjot Singh, “A Novel Wavelet Based Image Fusion for BrainTumor Detection”,International Journal of computer vision and signal processing,2(1),2013.
N.Rajalakshmi,V.LakshmiPrabha,”Automated classification of Brain MRI using color K-Means clustering segmentation and application of different kernel functions with multiclass SVM,” 1st Annual International Interdisciplinary Conference, AIIC 2013, 24-26 April, Azores, Portugal.
 Anjali A.Pure, Neelesh Gupta, Meha Sri Vatsava,” A New Image Fusion Method based on Integration of Wavelet and Fast Discrete Curvelet Transform” International Journal of Computer Applications. Volume 69 May 2013.P.Gladis,
 Pushpa Rathi and Dr.S.Pilani,”Brain Tumor MRI image classification with feature selection and extraction using linear discriminant analysis,” International Journal of Information Sciences and Techniques, Vol.2, No.4, July 2012.
 Shaheen Ahmed, Khan M. Iftekharuddin,” Efficacy of Texture, Shape, and Intensity Feature Fusion for Posterior-Fossa Tumor Segmentation in MRI,” IEEE transactions on Information technology in Biomedicine, Volume 15,March 2011.
 A.Padma R.Sukanesh,”Automatic Classification and segmentation of Brain Tumor in CT images using Optimal gray level length run length texture features,” International Journal of Advanced Computer Science and Applications, Vol. 2, No. 10, 2011.
 Chetan K. Solanki Narendra M. Patel, “Pixel based and Wavelet based Image fusion Methods with their Comparative Study”. National Conference on Recent Trends in Engineering & Technology. 13-14 May 2011
 M .Chandana,S. Amutha, and Naveen Kumar, “ A Hybrid Multi-focus Medical Image Fusion Based on Wavelet Transform”. International Journal of Research and Reviews in Computer Science (IJRRCS) Vol.2, No. 4, August 2011, ISSN: 2079-2557.
K.Kannan,S.Arumuga Perumal and K.Arulmozhi. “The Review of feature Level fusion of Multi- focused images using Wavelets. Recent Patents on Signal Processing, 2010, 2, 28-38.
 V.P.S. Naidu and J.R. Raol, “Pixel-level Image Fusion using Wavelets and Principal Component Analysis”. Defence Science Journal, Vol. 58, No. 3, May 2008, pp. 338-352 ,2008.
 Bruno Alphano ,Mario Ciampi and Giuseppe De Pietro,” A Wavelet Based Algorithm for Multi Modal Medical Image Fusion” Springer-Verlag Berlin Heidelberg 2007,SAMT 2007,LNCS 4816,pp. 117-120,2007.
Brain Tumor, Multimodal, Neural Network.