Effectiveness of the FCM Clustering Algorithm for Detection of Brain Tumor Using Image Segmentation
Abstract
Brain tumor is one of
the dangerous and critical disease which is even hard to get detected without
any medical test for example CT scan, MRI Scan. Image segmentation is a process
that works by segregating any arbitrary image into non-intersecting regions.
The regions obtained after the division should be such that each region is
homogenous and the union of any two adjacent regions is heterogeneous. New
image segmentation is a method by combining the FCM clustering algorithm with a
rough set theory. The rough-fuzzy c-means algorithm is presented for
segmentation of brain images. The main focus of the work, based on human MRI
brain image, is to optimize the segmentation process with higher accuracy rate.
Cluster analysis recognizes collections of comparable objects and therefore
helps in learning circulation of outlines in big data sets. Clustering is most
widely used for real world applications. The effectiveness of the FCM
algorithm, along with a comparison with other related algorithms, is
demonstrated on a set of brain images. Rough set theory can be useful method to
overcome such complication during image segmentation. Because of this we will
use FCM clustering algorithm with a rough set theory.
Country : India
1 Dr. Janardhan Antharam
Professor, Department of Computer Science and Engineering, Malla Reddy College of Engineering for Women, Hyderabad -500100, Telangana, India
IRJIET, Volume 2, Issue 10, December 2018 pp. 24-27
X. Yong, Z. Ji, and Y. Zhang, “Brain
MRI image segmentation based on learning local variational Gaussian mixture
models”, Sep. 2016.
Rosita Shishegar1,2 Anand A. Joshi3
Mary Tolcos4,5 David W. Walker5 Leigh A. Johnston1,6, “Automatic segmentation
of fetal brain using diffusion-weighted imaging cues”, 2017.
Christoph Matthies, Franziska
Dobrigkeit, Guenter Hesse,“InfiNet Fully convolutional networks for infant
brain MRI segmentation”,2018.
Lata Ayesha Akter and Goo-Rak Kwon,
“Integration of Contourlet Transform and Canny Edge Detector for Brain Image
Segmentation”, 2018.
K. Y. Lim and R. Mandava, “A
multi-phase semi-automatic approach for multisequence brain tumor image
segmentation”,Dec 2018.
Namburu, S. K. Samay, and S. R.
Edara, “Soft fuzzy rough set-based MR brain image segmentation”, May-2017.
Herng-Hua Chang and Chih-Chung Hsieh,
“Brain segmentation in MR images using a texture-based classifier associated
with mathematical morphology”, 2017.
