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Expounding the MRI Sequences for Computer Aided Diagnosis for Detection of Brain Tumors

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Ashwini S. Shinde, Veena V. Desai
10.5120/ijca2017914934

Ashwini S Shinde and Veena V Desai. Expounding the MRI Sequences for Computer Aided Diagnosis for Detection of Brain Tumors. International Journal of Computer Applications 170(9):17-21, July 2017. BibTeX

@article{10.5120/ijca2017914934,
	author = {Ashwini S. Shinde and Veena V. Desai},
	title = {Expounding the MRI Sequences for Computer Aided Diagnosis for Detection of Brain Tumors},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {170},
	number = {9},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {17-21},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume170/number9/28098-2017914934},
	doi = {10.5120/ijca2017914934},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Abnormal and uncontrollable growth of the cells causes tumors .Early diagnosis by the physician and proper treatment of the tumors are essential for the prevention of permanent damage of the affected area and so also prevents death. The soft tissues of the body get affected by tumors, brain is one of the commonly affected areas with tumor .The Magnetic Resonance Imaging (MRI) is one of the power full techniques mainly used for detection of tumors. It is a radiation-based technique which represents the internal structure of the body in terms of intensity variations that are radiated by the biological system when exposed to Radio Frequency (RF). When the brain images are inspected or interpreted one should be aware of the image contrast since the entire information about brain is mapped into intensity variations of the brain MRI images captured during image acquisition the artifacts introduced affect the quality of analysis the physician also needs the quantification of the tumor area [1] hence it is required to an efficient rectifying methodology for removal of these artifacts present in the image before diagnosis. Here in this paper attempt is made to explain the different Sequences of Brain MRI and also enlighten the different computer aided techniques used for segmentation, and bring forward one of the method for tumor detection after Preprocessing.

References

  1. El-Sayed A. El-Dahshan, Heba M. Mohsen, Kenneth Revett, Abdel-Badeeh M. Salem, 2014“Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm” ELSEVIER, www.elsevier.com/locate/eswa
  2. SamirBARA, Hasan EL MAIA, Ahmed HAMMOUCH, Driss ABOUTAJDIE.2014 “A Robust Approach for the Detection of Brain tumors by Variational B-Spline Level-Set Methodand brain extraction” 978-1-4799-3824-7/14/$31.00 ©2014 IEEE.
  3. Steven l.Horowitz, Theodosios Pavliss 1976 “Picture Segmentation by a Tree Traversal Algorithm” Journal of the A~ochttion for Computing Machinery. Vol. 23, No. 2, Aprd 1976, pp. 368~388
  4. Mohsen, H., El-Dahshan, E., & Salem, A. (2012). A machine learning technique for MRI brain images. In Proceedings of the (8th) 2012 INFOS IEEE international conference on informatics and systems (INFOS2012). IEEE.
  5. Jafari, M., & Kasaei, S. (2011). Automatic brain tissue detection in MRI images using seeded region growing segmentation and neural network classification. Australian Journal of Basic and Applied Sciences, 5(8), 1066–1079.
  6. P. Shanthakumar, P. Ganeshkumar , “Performance analysis of classifier for brain tomor detection and diagnosis”, in Elsevier,vol 45,July 2015, pp 302-311
  7. Samir BARA, Hasan EL MAIA, Ahmed HAMMOUCH, Driss ABOUTAJDIE “A Robust Approach for the Detection of Brain tumors by Variational B-Spline Level-Set Method and brain extraction”. 978-1-4799-3824-7/14/$31.00 ©2014 IEEE
  8. Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
  9. Clustering - K-means [Online] https://home.deib.polimi.it/matterucc/Clustering/tutorial html/k means.html
  10. The Whole Brain Atlas - Harvard Medical School [Online] http://www.med.harvard.edu/aanlib/home.html

Keywords

Magnetic Resonance Imaging, T1/T2weighted, FLAIR, Preprocessing Thresholding, Noise Removal.