Design and Development of Algorithms for Enhancement of Brain Tumor from Medical Images

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2019 by IJETT Journal
Volume-67 Issue-3
Year of Publication : 2019
Authors : Krishna Prajapati, Priya Kansagra, Utsav Patel, Dimpal Khambhati
  10.14445/22315381/IJETT-V67I3P227

MLA 

MLA Style: Krishna Prajapati, Priya Kansagra, Utsav Patel, Dimpal Khambhati "Design and Development of Algorithms for Enhancement of Brain Tumor from Medical Images" International Journal of Engineering Trends and Technology 67.3 (2019): 141-145.

APA Style: Krishna Prajapati, Priya Kansagra, Utsav Patel, Dimpal Khambhati (2019). Design and Development of Algorithms for Enhancement of Brain Tumor from Medical Images. International Journal of Engineering Trends and Technology, 67(3), 141-145.

Abstract
Medical image processing technology is evolving and have its roots in the past where the bony tissues and the bones can be easily viewed through many diagnostic equipment like computed tomography. Computed tomography also known as CT scan is an imaging technique that helps to get 3D-structure of any part of the body through multiple cross-sectional areas and help detect any abnormality. CT scan being non-invasive and simplest diagnostic method can help to detect abnormal tissue growth mainly which is of the cancerous cells in the brain. This abnormal tissue growth in the brain which can cease normal functions of the brain over a long period of time results to having brain tumour. Studying the tumour when detected is very tedious and becomes challenging and hence we have designed various algorithms in MATLAB that can make this job a bit easier and can help detect the level of threat that the patient is at and what kind of treatment and medications should one undergo.

Reference
[1] W.Gonzalez, “Digital Image Processing”, 2nd ed. Prentice Hall, Year of Publication 2008.
[2] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, Second Edition.
[3] Rafael C. Gonzalez, Richard E. Woods, Steve L. Eddins, Digital Image Processing Using MATLAB, 2003.
[4] Rania Hussien Al-Ashwal, EkoSupriyanto, et.al., “Digital Processing for Computed Tomography Images: Brain Tumor Extraction and Histogram Analysis”, Mathematics and Computers in Contemporary Science, 2013
[5] J. Selvakumar, A. Lakshmi and T. Arivoli, “Brain Tumor Segmentation and its area calculation in Brain MR images using K-means clustering and fuzzy C-mean algorithm”, International Conference on Advances in Engineering, Science and Management, 2012
[6] R. Rajeswari, P. Anadhakumar, “Image segmentation and identification of brain tumor using FFT techniques of MRI images”, ACEEE International Journal on Communication, Vol. 02, No. 02, July 2011
[7] Mustaqeem, Anam, Ali Javed, and Tehseen Fatima, “An efficient brain tumor detection algorithm using watershed and thresholding-based segmentation”, International Journal 4, 2012
[8] P.Dhanalakshmi, T.Kanimozhi, “Automated Segmentation of Brain Tumor using K-Means Clustering and its area calculation”, IJAEEE, 2013.
[9] Q. Hu, G. Quian, A. Aziz, W.L. Nowinski,” Segmentation of Brain from Computed Tomography head images,” Engineering in Medicine and Biology 27th Annual Conference, 2005.
[10] NatrajanP., Krishnan N., Natasha Sandeep kenkre and et.al ,”Tumor Detection using Threshold operation in MRI Brain Images,” IEEE International Conference on Computational Intelligence and Computing Research, 2012.
[11] P. Natrajan, Debsmita Ghosh, kenkre Natasha Sandeep, Sabiha Jilani,” Detection of Tumor in Mammogram Images using extended Local Minima Threshold,” International Journal of Engineering and Technology, Vol. 5, No. 3, jun-jul 2013.
[12] X. Zang, J.Yang, D.Weng, Y. Liu and Y. Wang, “A novel anatomical Structure segmentation method of CT head images,” International Conferences on complex medical Engineering, 2010.
[13] A. Padma and R. Sukanesh, “Automatic Classification and segmentation of brain tumor in CT images using optimal dominant gray level run length texture features,” Internationaljournal of Advanced Computer Science and Applications, 2011.
[14] R.C. Patil and Dr. A.S. Bhalachandra, “Brain tumour extraction from MRI images using MATLAB,” International Journal of Electronics, Communication &Soft Computing Science and Engineering,vol. 2, pp. 1-4.
[15] A.Yusuf, Z.Sufyanu, K.Mamman “proposed magnetic resonance imaging for brain tumor analysis” European Journal of Advances in Engineering and Technology, 2005.
[16] H.P.Agustin “brain tumor image segmentaton in mri image” International Conference on Electrical Engineering, 2012.
[17] C.Malathy ,N.Kundu, S.Sad “survey on brain tumor identification” International Journal of Engineering and Technology, 2012.
[18] M.Kaur, Dr.R.Mittal “survey of intelligent methods for brain tumor detection” International Journal of Computer Science Issues, 2009.
[19] P.Pati, S.Pawar, Ms. SunaynaPati “paper on brain tumor segmentation and detection” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 2012.
[20] A.Tjahyaningtijas “segmentation of brain tumor in mri images” IOP conference, 2010
[21] Y.Assaf “diffusion tensor imaging (dti)-based white mattermapping in brain research” J MolNeurosci, 2008
[22] M.J.Mcauliffe, F.M. Lalonde “medical image processing, analysis and visualisation in clinical research.” Journal of Applied Science Research, 2014.

Keywords
Computed tomography, Brain tumor, Algorithm, Image processing