Open Access
MATEC Web of Conferences
Volume 57, 2016
4th International Conference on Advancements in Engineering & Technology (ICAET-2016)
Article Number 02011
Number of page(s) 6
Section Information Systems & Computer Science Engineering
Published online 11 May 2016
  1. T. Rajesh, R. Suja Mani Malar, (2013) “Rough Set Theory and Feed Forward Neural”, international conference on advance nanomaterials and emerging engineering technologies (ICANMEET), pp 240-244..
  2. Atiq Islam, Syed M. S. Reza, and Khan M. Iftekharuddin,(2013) “Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors” IEEE Transactions On Biomedical Engineering, Vol. 60, No. 11, pp 3204-3215. [CrossRef]
  3. Eyup Emre Ulku, Ali Yilmaz Camurcu, (2013) “Computer Aided Brain Tumor Detection With Histogram Equalization And Morphological Image Processing Techniques”, ICECCO, pp 48-51.
  4. P Dvorak, W Kropatsch, and K Bartusek ,(2013) “Automatic Detection of Brain Tumors in MR Images”, Telecommunication and Signal Processing, pp 577-5802.
  5. J. Vijay, J. Subhashini,(2013) “An Efficient Brain Tumor Detection Methodology using k-means clustering algorithm” International conference on Communication and Signal Processing, pp 653-657.
  6. I Maiti , Dr. Monisha Chakraborty, (2012) ,“A New Method for Brain Tumor Segmentation Based on Watershed and Edge Detection Algorithms in HSV Colour Model”, Computing and communication systems (NCCCS), pp 1-5.
  7. Natarajan P, Krishnan.N, Natasha Sandeep Kenkre, Shraiya Nancy, Bhuvanesh Pratap Singh, “Tumor Detection using threshold operation in MRI Brain Images”, Computational intelligence and computing research (ICCIC) IEEE International conference, pp 1-4.
  8. Azian Azamimi Abdullah, Bu Sze Chize,(2012) Yoshifumi Nishio, “Implementation of An Improved Cellular Neural Network Algorithm For Brain Tumor Detection”, International Conference on Biomedical Engineering (ICOBE), pp 611-615.
  9. China, Phooi Yee Lau, Frank C. T. Voon, and Shinji Ozawa, (2009)“The detection and visualization of brain tumors on T2-weighted MRI images using multiparameter feature blocks”, Engineering in Medicine and Biology 27th Annual Conference, pp 5104-5107.
  10. P. Singh, (2013) “A new approach to image segmentation”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, No. 4..
  11. H. G. Kaganami and Z. Beij,(2009) “Region based detection versus edge detection,” IEEE Transactions on Intelligent Information Hiding and Multimedia Signal Processing, pp. 1217-1221.
  12. X. iang, R. Zhang, and S. Nie,(2009) “Image segmentation based on PDEs model: A survey”, in Proc. 3rd International Conference on Bioinformatics and Biomedical Engineering, pp. 1-4.
  13. B. C. Wei, R. Mandava, (2010) “Multiobjective optimization Approaches in Image Segmentation- the Direction and Challenges”, ICSRS Publication.
  14. W.D. Heiss, P. Raab, and H. Lanfermann, (2011)“Multimodality assessment of brain tumors and tumor recurrence,” J. Nucl. Med., Vol. 52, No. 10, pp. 1585–1600. [CrossRef]
  15. Dongjin Kwon, (2014) “PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration” ieee transactions on medical imaging”, Vol. 33, No. 3, pp. 0278-0062. [CrossRef]