TY  - JOUR
T1  - Segmentation of Brain Tumor Using K-Means Clustering Algorithm
AU - Vijaya Kumar, D. AU - Jaya Rama Krishniah, V.V. 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 11
SP  - 3942
EP  - 3945
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.3942.3945
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.3942.3945
KW  - Magnetic Resonance Imaging (MRI)
KW  -CT (Computerized Tomoghraphy) scan
KW  -image segmentation
KW  -brain tumor
KW  -k-means
KW  -thresholding
AB  - Since, image segmentation is a classic inverse problem which consist of achieving a compact region
based description of the image scene by decomposing it into meaningful or spatially regions sharing similar
attributes. Tumor is nothing but uncontrolled growth of tissues in any part of the body. Tumors are of different
types and they have different treatments. The k-means algorithm is an iterative technique that is used to
partition an image into k clusters. In developed countries most research show that the number of people who
have brain tumors were died due to inaccurate detection of tumor. CT scan or MRI is directed into intracranial
cavity produces a complete image of brain and this image is visually examined for detection.
ER  -