Volume 8 Number 1 (Jan. 2018)
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IJBBB 2018 Vol.8(1): 1-10 ISSN: 2010-3638
doi: 10.17706/ijbbb.2018.8.1.1-10

Comparative Study of Segmentation Techniques for Detection of Tumors Based on MRI Brain Images

Ashraf M. Said, Fatma S. Ibrahim
Abstract—The aim of this paper is to propose a matlab toolbox of a comparative study of four brain tumor segmentation methods with specific sequences. Four methods are K-means clustering, fuzzy C-means clustering, Region growing, and Otsu segmentation. Skull stripping algorithm has done before segmentation. Different preprocessing algorithms are applied before segmentation to enhance images and give better segmentation. Median filter, Gaussian filter, and contrast enhancement are applied according to segmentation method. The database used is based on DICOM images. GLCM used for feature extraction. Classification and accuracy measurement are done by ANN.

Index Terms—ANN, fuzzy C-means, GLCM, K-means clustering, otsu segmentation, region growing.

The authors are with Minia University /Biomedical Engineering, Minia, Egypt (email: fatmasayed93@gmail.com).

Cite: Ashraf M. Said, Fatma S. Ibrahim, "Comparative Study of Segmentation Techniques for Detection of Tumors Based on MRI Brain Images," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 8, no. 1, pp. 1-10, 2018.

General Information

ISSN: 2010-3638
Frequency: Bimonthly (2011-2015); Quarterly (Since 2016)
DOI: 10.17706/IJBBB
Editor-in-Chief: Prof. Ebtisam Heikal 
Abstracting/ Indexing: Electronic Journals Library, Chemical Abstracts Services (CAS), Engineering & Technology Digital Library, Google Scholar, and ProQuest.
E-mail: ijbbb@iap.org
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