Segmentation and visualization of brain lesions in multispectral magnetic resonance images.
In this study we focus on the problem of segmentation and visualization of soft tissue structures in three-dimensional (3D) magnetic resonance (MR) imaging. We introduce a classification method which is a combination of a recently proposed contour detection algorithm and Haslett's contextual classification method extended to 3D. This classification method is used in the classification step of a rendering model suggested by Drebin et al. for visualizing normal and pathological tissue structures in the brain. We evaluate the combination of these two methodologies, and identify some problems which have to be solved in order to develop a clinical useful tool.
Holden
M
M
Department of Pattern Recognition and Image Processing, Norwegian Computing Center, Oslo.
Steen
E
E
Lundervold
A
A
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000473:pathology; D001927:Brain Diseases / Q000175:diagnosis* / Q000473:pathology; D001932:Brain Neoplasms / Q000175:diagnosis / Q000556:secondary; D002283:Carcinoma, Bronchogenic / Q000175:diagnosis / Q000556:secondary; D003287:Contrast Media; D003626:Data Display; D005682:Gadolinium; D019786:Gadolinium DTPA; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000145:classification / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000145:classification / Q000379:methods; D008297:Male; D008875:Middle Aged; D009942:Organometallic Compounds; D010363:Pattern Recognition, Automated; D004369:Pentetic Acid / Q000031:analogs & derivatives
D003287:Contrast Media; D009942:Organometallic Compounds; D004369:Pentetic Acid; D005682:Gadolinium; D019786:Gadolinium DTPA