Tumor Location and size Identification in Brain Tissues Using Fuzzy C- Clustering and Artificial Bee Colony Algorithm

  Mihir A. Mishra,  Pragnesh A. Patel,  Kushal D. Patel

Clustering approach is widely used in biomedical applications particularly for brain tumor detection in abnormal magnetic resonance (MRI) images. Fuzzy clustering using fuzzy C-means (FCM) algorithm proved to be superior over the other clustering approaches in terms of segmentation efficiency.MRI Imaging forms one of the core methods to identify Brain Tumors, and access the existence, size and thickness of the tumor. MRI Images are prone to high noise, as the whole principle works on strong electric fields.We forms the clusters using Fuzzy C-Means clustering and then identify the tumor part by removing the noise part using Artificial Bee Colony algorithm. Many image processing techniques have been proposed for brain MRI segmentation, most notably thresholding,region-growing, and clustering. Since the distribution of tissue intensities in brain images is very complex, it leads to difficulties of threshold determination.

Keywords- Clustering, MR brain tumor, Fuzzy C-means and Segmentation efficiency, Artificial Bee Colony

Unique Identification Number - IJEDR1602310
Page Number(s) - 1760-1766
Pubished in - Volume 4 | Issue 2 | June 2016
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Mihir A. Mishra,  Pragnesh A. Patel,  Kushal D. Patel,   "Tumor Location and size Identification in Brain Tissues Using Fuzzy C- Clustering and Artificial Bee Colony Algorithm", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.4, Issue 2, pp.1760-1766, June 2016, Available at :http://www.ijedr.org/papers/IJEDR1602310.pdf