Brain tumor segmentation using k-means–FCM hybrid technique

dc.contributor.author	Vaibhavi, Patel
dc.contributor.author	Rupal, Kapdi
dc.date.accessioned	2021-12-14T10:27:45Z
dc.date.available	2021-12-14T10:27:45Z
dc.date.issued	2017-03
dc.identifier.issn	978-981-10-7386-1
dc.identifier.uri	http://27.109.7.66:8080/xmlui/handle/123456789/749
dc.description	Ambient Communications and Computer Systems pp 341-352	en_US
dc.description.abstract	Automatic brain tumor segmentation and detection is always very challenging and difficult task with respect to accuracy which is more important as brain surgery is a critical and complicated process. The medical professional can interpret magnetic resonance images (MRI), but this task is time-consuming, error-prone and tedious. So automatic segmentation technique is needed which is the unsolved challenging problem. In this paper, study of the different algorithms used for the brain tumor segmentation is done and a hybrid algorithm of K-means and FCM algorithm is implemented. The result of proposed algorithm is compared with the individual results of K-means and FCM algorithm.	en_US
dc.language.iso	en	en_US
dc.publisher	Springer	en_US
dc.subject	FCM K means	en_US
dc.subject	MRI CT scan	en_US
dc.subject	Correspondence ratio	en_US
dc.subject	Percentage match	en_US
dc.title	Brain tumor segmentation using k-means–FCM hybrid technique	en_US
dc.type	Article	en_US