Brain Tumor Detection Using Adaptive K-Means Clustering Segmentation
| Author(s) | : | B.Lalitha, Prof T.Ramashri |
| Institution | : | Department of ECE, Sri Venkateswara University , Tirupati, A.P, India |
| Published In | : | Vol. 4, Issue 7 — July 2017 |
| Page No. | : | 597-601 |
| Domain | : | Engineering |
| Type | : | Research Paper |
| ISSN (Online) | : | 2348-4470 |
| ISSN (Print) | : | 2348-6406 |
Magnetic resonance imaging (MRI) is widely preferred technique to access the Brain tumor, But Dueto large amount of data produced by MRI prevents manual segmentation in a reasonable time. So, efficient segmentationmethods are required for MRI Brain tumor detection. The watershed transform has interesting property and is popularthat make it useful for many segmentation application. The drawback associated with watershed transform is the oversegmentation which results in MRI brain image. In this paper an adaptive K-Means clustering algorithm is used fordetection of brain tumor on segmentation and morphological operator. The proposed method allows the segmentation oftumor tissues with accuracy compared to manual segmentation. The quantitative and visual segmentation result showsthe superiority of the proposed method.
B.Lalitha, Prof T.Ramashri, “Brain Tumor Detection Using Adaptive K-Means Clustering Segmentation”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 7, pp. 597-601, July 2017.
