DocumentCode :
3378140
Title :
Automatic detection of brain tumor based on magnetic resonance image using CAD System with watershed segmentation
Author :
Vrji, K. S. Angel ; JayaKumari, J.
Author_Institution :
CSE, Noorul Islam Center for Higher Educ., Kumaracoil, India
fYear :
2011
fDate :
21-22 July 2011
Firstpage :
145
Lastpage :
150
Abstract :
In medical image processing Segmentation of anatomical regions of the brain is the fundamental problem. Here, a brain tumor segmentation method has been developed and validated using MRI Data. In Preprocessing and Enhancement stage, medical image is converted into standard formatted image. Segmentation subdivides an image into its constituent regions or objects. This method can segment a tumor provided that the desired parameters are set properly. In this paper, after a manual segmentation procedure the tumor identification, the investigations has been made for the potential use of MRI data for improving brain tumor shape approximation and 2D & 3D visualization for surgical planning and assessing tumor.
Keywords :
CAD; approximation theory; biomedical MRI; brain; data visualisation; image enhancement; image segmentation; medical image processing; surgery; tumours; 2D visualization; 3D visualization; CAD system; anatomical brain region segmentation; automatic brain tumor detection; brain tumor segmentation method; brain tumor shape approximation; enhancement stage; magnetic resonance image; manual segmentation procedure; medical image processing; preprocessing stage; surgical planning; tumor assessment; tumor identification; watershed segmentation; Data visualization; Image edge detection; Image segmentation; Magnetic resonance imaging; Signal processing; Three dimensional displays; Tumors; Magnetic Resonance Image (MRI); Segmentation; watershed segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), 2011 International Conference on
Conference_Location :
Thuckafay
Print_ISBN :
978-1-61284-654-5
Type :
conf
DOI :
10.1109/ICSCCN.2011.6024532
Filename :
6024532
Link To Document :
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