Comparative study of techniques for brain tumor segmentation

Author

Tulsani, Hemant ; Saxena, Saransh ; Bharadwaj, Mamta

Author_Institution

Electron. & Commun. Dept., Ambedkar Inst. of Adv. Commun. Technol. & Res., New Delhi, India

fYear

2013

Firstpage

117

Lastpage

120

Abstract

In this paper, we present a comparative study of various techniques which have been proposed for segmentation of brain tumors in MRI data. Three different techniques are discussed in this paper. These include morphological watershed segmentation, K-Means and Fuzzy C-means clustering. In watershed technique, marker is used for tumor segmentation. Clustering is a technique for reducing the number of objects in the data set. K-Means and Fuzzy C-Means clustering algorithms are discussed in this paper. K-Means used an objective function for clustering while Fuzzy C-Means comes under the category of soft segmentation technique. Simulation are dome in MATLAB 2013a and results for the techniques are discussed.

Keywords

biomedical MRI; brain; fuzzy set theory; image segmentation; mathematical morphology; medical image processing; pattern clustering; tumours; K-means clustering algorithm; MRI; brain tumor segmentation; fuzzy C-means clustering algorithm; morphological watershed segmentation; soft segmentation technique; IEEE Xplore; Portable document format;

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia, Signal Processing and Communication Technologies (IMPACT), 2013 International Conference on

Conference_Location

Aligarh

Print_ISBN

978-1-4799-1202-5

Type

conf

DOI

10.1109/MSPCT.2013.6782100

Filename

6782100