TY - GEN
T1 - Efficient fuzzy clustering based approach to brain tumor segmentation on MR images
AU - Arakeri, Megha P.
AU - Ram Mohana Reddy, G.
PY - 2011
Y1 - 2011
N2 - Image segmentation is one of the most vital and significant step in medical applications. The conventional fuzzy c-means (FCM) clustering is the most widely used unsupervised clustering method for brain tumor segmentation on magnetic resonance (MR) images. However, the major limitation of the conventional FCM is its huge computational time and it is sensitive to initial cluster centers. In this paper, we present a novel efficient FCM algorithm to eliminate the drawback of conventional FCM. The proposed algorithm is formulated by incorporating distribution of the gray level information in the image and a new objective function which ensures better stability and compactness of clusters. Experiments are conducted on brain MR images to investigate the effectiveness of the proposed method in segmenting brain tumor. The conventional FCM and the proposed method are compared to explore the efficiency and accuracy of the proposed method.
AB - Image segmentation is one of the most vital and significant step in medical applications. The conventional fuzzy c-means (FCM) clustering is the most widely used unsupervised clustering method for brain tumor segmentation on magnetic resonance (MR) images. However, the major limitation of the conventional FCM is its huge computational time and it is sensitive to initial cluster centers. In this paper, we present a novel efficient FCM algorithm to eliminate the drawback of conventional FCM. The proposed algorithm is formulated by incorporating distribution of the gray level information in the image and a new objective function which ensures better stability and compactness of clusters. Experiments are conducted on brain MR images to investigate the effectiveness of the proposed method in segmenting brain tumor. The conventional FCM and the proposed method are compared to explore the efficiency and accuracy of the proposed method.
UR - https://www.scopus.com/pages/publications/84055176552
UR - https://www.scopus.com/pages/publications/84055176552#tab=citedBy
U2 - 10.1007/978-3-642-25734-6_141
DO - 10.1007/978-3-642-25734-6_141
M3 - Conference contribution
AN - SCOPUS:84055176552
SN - 9783642257339
T3 - Communications in Computer and Information Science
SP - 790
EP - 795
BT - Computational Intelligence and Information Technology - First International Conference, CIIT 2011, Proceedings
T2 - 1st International Conference on Computational Intelligence and Information Technology, CIIT 2011
Y2 - 7 November 2011 through 8 November 2011
ER -