Title :
Anatomical structure segmentation in MRI brain images
Author :
Lakshmi, G. Geethu ; Suruliandi, A.
Author_Institution :
Comput. Sci. & Eng., Manomaniam Sundaranar Univ., Thirunelveli, India
Abstract :
A standard segmentation problem within Magnetic Resonance Imaging (MRI) is the task of labelling voxels according to their tissue type that are White Matter (WM), Gray Matter (GM), and Cerebrospinal fluid (CSF).Image segmentation provides volumetric quantification of cortical atrophy and thus helps in the diagnosis of degenerative diseases such as Epilepsy, Schizophrenia, Alzheimer´s disease, Dementia and Hydrocephalus. This work deals with comparison of segmentation results by applying K-Means algorithm after morphological skull stripping and also by using the Brain Extraction Tool(BET) for skull stripping. The results are compared using the parameters such as Mean Square Error(MSE), Peak Signal to Noise Ratio(PSNR) and percentage of area segmented.
Keywords :
biological tissues; biomedical MRI; brain; diseases; image segmentation; medical image processing; pattern clustering; Alzheimer´s disease; Brain Extraction Tool; K-means algorithm; MRI brain images; Schizophrenia; anatomical structure segmentation; cerebrospinal fluid; cortical atrophy; degenerative diseases; dementia; epilepsy; gray matter; hydrocephalus; image segmentation; magnetic resonance imaging; mean square error; morphological skull stripping; peak signal to noise ratio; tissue type; volumetric quantification; voxels labelling; white matter; Alzheimer´s disease; Clustering algorithms; Image segmentation; Magnetic resonance imaging; Monitoring; PSNR; Skull; Brain Extraction tool; Brain Segmentation; K-Means clustering; Morphological skull stripping;
Conference_Titel :
Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on
Conference_Location :
Tamil Nadu
Print_ISBN :
978-1-4244-7923-8
DOI :
10.1109/ICETECT.2011.5760225
