A Novel Hybrid Approach Using Kmeans Clustering and Threshold filter for Brain Tumor Detection
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - Issue 2013 by IJCTT Journal|
|Volume-4 Issue-3 |
|Year of Publication : 2013|
|Authors :S.S. Mankikar|
S.S. Mankikar"A Novel Hybrid Approach Using Kmeans Clustering and Threshold filter for Brain Tumor Detection"International Journal of Computer Trends and Technology (IJCTT),V4(3):206-209 Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: - — Medical imaging makes use of the technology to disclose the internal structure of the human body. By means of medical imaging modalities patient’s life can be better through a accurate and quick treatment without any side effects. The foremost purpose of this paper is to develop an automated framework that can accurately classify a tumor from abnormal tissues. In this paper, we put forward a hybrid framework that uses the K-means clustering followed by Threshold filter to track down the tumor objects in magnetic resonance (MR) brain images. The main concept in this hybrid framework is to separate the position of tumor objects from other items of an MR image by using Kmeans clustering and Threshold filter. Experiments reveal that the method can successfully achieve segmentation for MR brain images to help pathologists distinguish exactly lesion size and region.
 J.Jaya, K.Thanushkodi ,M.Karnan,.”Tracking Algorithm for De-Noising of MR Brain Images “
 Dhawan, A. P., “A Review on Biomedical Image Processing and Future Trends,” Computer Methods and Programs in Biomedicine, Vol. 31, No.3-4, 1990, pp.141-183.
 Gonzalez, R. C.; Woods, R. E., Digital Image Processing, 2nd ed., Prentice-Hall, Englewood Cliffs, NJ, 2002.
 Tsai, C. S., Chang, C. C., “An Improvement to Image Segment Based on Human Visual System for Object-based Coding,” Fundamental Informaticae, Vol. 58, No. 2, 2004, pp.167-178
Keywords— Brain tumor detection, Kmeans clustering, MR image, Segmentation, Threshold filter.