Feature Extraction of the Brain Tumours with the help of MRI, based on Symmetry and partitioning

Pratima Gumaste, Vinayak Bairagi

Abstract


Computer-aided diagnostic (CAD) studies are used for scientific observations for explanation since very long time, but they are extraordinarily powerful to perform completely machine-driven algorithmic analyses for brain magnetic resonance imaging lesions. Structural and purposeful imbalance within the human brain could be reviewed. This imbalance analysis of the brain has terrific importance in an image analysis. In the present work, the imbalance between the two hemispheres is considered as the base for the detection of the tumour. We have segmented the brain into the two halves using thresholding technique, followed by statistical feature extraction for the double authentication of the existence of tumour which proves to be the better approach. The approach also takes into consideration corrections needed for the tilt observed while capturing the MRI.

Keywords


MRI; Mid-sagittal plane; symmetry; T2 weighted Images

Full Text:

PDF

References


Yanxi Liu, Robert T. Collins, and William E. Rothfus, “Robust Midsagittal Plane Extraction from Normal and Pathological 3-D Neuroradiology Images,”, IEEE Transactions On Medical Imaging, VOL. 20, NO. 3, pp 175 – 192, MARCH 2001

Xuguang Qi, Ashwin Belle, Sharad Shandilya, Wenan Chen, Charles Cockrell, Yang Tang, Kevin R. Ward, Rosalyn H. Hargraves, Kayvan Najarian, “Ideal Midline Detection Using Automated Processing of Brain CT Image”, Open Journal of Medical Imaging, pp 51-59, 2013.

Dvorak, P, Brno, Czech Republic ; Kropatsch, W. ; Bartusek, K., “Automatic detection of brain tumors in MR images” Telecommunications and Signal Processing (TSP), 36th International Conference., pp 577 – 580, 2-4 July 2013

Sheena Xin Liu, “Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature”, Journal of Biomedical Informatics, pp 1056–1064, 2009.

Dong-Hyun Kim and Soo-Young Ye, “CAD for Detection of Brain Tumor Using the Symmetry Contribution From MR Image Applying Unsharp Mask”, Transactions On Electrical And Electronic Materials, Vol. 15, No. 4, pp. 230-234, August 25, 2014

H.J. Kuijf, S.J. van Veluw, M.I. Geerlings, M.A. Viergever, G.J. Biessels, K.L. Vincken, “Automatic extraction of the midsagittal surface from brain MR images using the Kullback Leibler measure", Neuroinformatics, nr. 3, vol. 12, pp. 395-403, 2014.

Surani Anuradha JayasuriyaAlan Wee-Chung Liew, Phillip Sheridan, “Symmetry Detection in Brain Image Analysis”, IGI Global/ Medical Technologies, pp 5615-5617, 2015.

Kirti Raj Bhatele, Sarita Singh Bhadauria, “Pixel based Symmetry Analysis of an Axial T2 Weighted Brain MRI”, IJCA, Volume 118 – No.24, pp 9-14, May 2015

Neeraj Sharma, Amit K. Ray, Shiru Sharma, K. K. Shukla, SatyajitPradhan, and Lalit M. Agarwal, “Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network”, J Med Phys. Jul-Sep; 33(3): 119–126, 2008.

Namita Aggarwal, R. K. Agrawal, “First and Second Order Statistics Features for Classification of Magnetic Resonance Brain Images”, Journal of Signal and Information Processing, pp.146-153, 2012

Xiao Xuan, Qingmin Liao, “Statistical Structure Analysis in MRI Brain Tumor Segmentation”, ICIG[Fourth International Conference on Image and Graphics], pp 421 – 426, 2007 IEEE.

Nooshin Nabizadeh, Miroslav Kubat, “Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features”, Elsevier, Computers and Electrical Engineering, pp 1 – 16, 2015.

Mussarat Yasmin, Sajjad Mohsin, Muhammad Sharif, MudassarRaza and Saleha Masood,“Brain Image Analysis: A Survey”, World Applied Sciences Journal 19 (10), ISSN 1818-4952: 1484-1494, 2012

R K Samantaray1, S B Panda, B Pradhan, “Automated Brain Tumor Detection and Identification Using Image Processing”, Researcher, http://www.sciencepub.net/researcher, pp 79-88, 2013.

Rash Bihari Dubey, Madasu Hanmandlu, Suresh K. Gupta, Sushil K. Gupta, “The Brain MR Image Segmentation Techniques and use of Diagnostic Packages”, Academic Radiology, Vol 17, No 5, pp 658 – 671, May 2010.

Natarajan P, Krishnan. N, Natasha Sandeep Kenkre, Shraiya Nancy, Bhuvanesh Pratap Singh, “Tumor Detection using threshold operation in MRI Brain Images ”, International Conference on Computational Intelligence and Computing Research, pp. 1-4, 2012 IEEE

Pavel Dvorak, Walter Kropatsch, and KarelBartusek, “Automatic Detection of Brain Tumors in MR Images”, IEEE TSP, pp. - 577 – 580, 2013




DOI: http://dx.doi.org/10.21533/pen.v7i3.312

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Pratima Gumaste

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License