%0 Generic %1 kawadiwale2014clustering %A Kawadiwale, Ramish B. %A and Milind E. Rane, %B Int. Conf. on Recent Trends in Information, Telecommunication and Computing , ITC %D 2014 %I ACEEE (A Computer division of IDES) %K Brain Clustering Hi K-means MR images %T Clustering Techniques for Brain Tumor Detection %U http://searchdl.org/public/conference/2014/ITC/49.pdf %X rain tumor is a malformed growth of cells within brain which may be cancerous or non-cancerous. The term ‘malformed’ indicates the existence of tumor. The tumor may be benign or malignant and it needs medical support for further classification. Brain tumor must be detected, diagnosed and evaluated in earliest stage. The medical problems become grave if tumor is detected at the later stage. Out of various technologies available for diagnosis of brain tumor, MRI is the preferred technology which enables the diagnosis and evaluation of brain tumor. The current work presents various clustering techniques that are employed to detect brain tumor. The classification involves classification of images into normal and malformed (if detected the tumor). The algorithm deals with steps such as preprocessing, segmentation, feature extraction and classification of MR brain images. Finally, the confirmatory step is specifying the tumor area by technique called region of interest

@conference{kawadiwale2014clustering, abstract = {rain tumor is a malformed growth of cells within brain which may be cancerous or non-cancerous. The term ‘malformed’ indicates the existence of tumor. The tumor may be benign or malignant and it needs medical support for further classification. Brain tumor must be detected, diagnosed and evaluated in earliest stage. The medical problems become grave if tumor is detected at the later stage. Out of various technologies available for diagnosis of brain tumor, MRI is the preferred technology which enables the diagnosis and evaluation of brain tumor. The current work presents various clustering techniques that are employed to detect brain tumor. The classification involves classification of images into normal and malformed (if detected the tumor). The algorithm deals with steps such as preprocessing, segmentation, feature extraction and classification of MR brain images. Finally, the confirmatory step is specifying the tumor area by technique called region of interest}, added-at = {2014-03-21T10:53:43.000+0100}, author = {Kawadiwale, Ramish B. and and Milind E. Rane}, biburl = {https://www.bibsonomy.org/bibtex/2c2d1eb89f79d73d22a245b265281e983/idescitation}, booktitle = {Int. Conf. on Recent Trends in Information, Telecommunication and Computing , ITC}, interhash = {644f21c846be3ea284275ac0b4c5c2e6}, intrahash = {c2d1eb89f79d73d22a245b265281e983}, keywords = {Brain Clustering Hi K-means MR images}, organization = {Institute of Doctors Engineers and Scientists}, publisher = {ACEEE (A Computer division of IDES)}, timestamp = {2014-03-21T10:53:43.000+0100}, title = {Clustering Techniques for Brain Tumor Detection}, url = {http://searchdl.org/public/conference/2014/ITC/49.pdf}, year = 2014 }