Title :
Unsupervised Segmentation of MRI using Independent Component Analysis
Author :
Özkurt, Nalan ; Özkurt, Ahmet
Author_Institution :
Dokuz Eylul Univ., Izmir
Abstract :
In this study, an autonomous classification and segmentation algorithm to diagnose and trace multiple sclerosis (MS), from magnetic resonance (MR) imaging is developed. In this method, new image stacks are derived by using three different weighted MR images, and then, independent components are obtained from those images derived. A decision maker is developed in order to choose the most suitable independent component by using spatial tone and MRI tissue properties.
Keywords :
biological tissues; biomedical MRI; decision making; image classification; image segmentation; independent component analysis; medical image processing; MRI; MRI tissue property; autonomous image classification; decision making; independent component analysis; magnetic resonance imaging; multiple sclerosis diagnosis; spatial tone; unsupervised image segmentation; Classification algorithms; Image segmentation; Independent component analysis; Magnetic resonance; Magnetic resonance imaging; Radio access networks;
Conference_Titel :
Signal Processing and Communications Applications, 2007. SIU 2007. IEEE 15th
Conference_Location :
Eskisehir
Print_ISBN :
1-4244-0719-2
Electronic_ISBN :
1-4244-0720-6
DOI :
10.1109/SIU.2007.4298651
