Title :
Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images
Author :
Lundervold, Arvid ; Storvik, Geir
Author_Institution :
Sect. for Med. Image Anal. & Pattern Recognition, Bergen Univ., Norway
fDate :
6/1/1995 12:00:00 AM
Abstract :
Presents a new method to segment brain parenchyma and cerebrospinal fluid spaces automatically in routine axial spin echo multispectral MR images. The algorithm simultaneously incorporates information about anatomical boundaries (shape) and tissue signature (grey scale) using a priori knowledge. The head and brain are divided into four regions and seven different tissue types. Each tissue type c is modeled by a multivariate Gaussian distribution N(μc,Σc). Each region is associated with a finite mixture density corresponding to its constituent tissue types. Initial estimates of tissue parameters {μc,Σc }c=1,…,7 are obtained from k-means clustering of a single slice used for training. The first algorithmic step uses the EM-algorithm for adjusting the initial tissue parameter estimates to the MR data of new patients. The second step uses a recently developed model of dynamic contours to detect three simply closed nonintersecting curves in the plane, constituting the arachnoid/dura mater boundary of the brain, the border between the subarachnoid space and brain parenchyma, and the inner border of the parenchyma toward the lateral ventricles. The model, which is formulated by energy functions in a Bayesian framework, incorporates a priori knowledge, smoothness constraints, and updated tissue type parameters. Satisfactory maximum a posteriori probability estimates of the closed contour curves defined by the model were found using simulated annealing
Keywords :
Gaussian distribution; biomedical NMR; brain; image segmentation; medical image processing; Bayesian framework; EM-algorithm; MRI segmentation; a priori knowledge; algorithmic step; arachnoid/dura mater boundary; brain parenchyma; cerebrospinal fluid; dynamic contours model; energy functions; finite mixture density; head; k-means clustering; medical diagnostic imaging; multispectral magnetic resonance images; multivariate Gaussian distribution; routine axial spin echo multispectral MR images; simply closed nonintersecting curves; simulated annealing; smoothness constraints; subarachnoid space; tissue parameters; Anatomy; Atrophy; Bayesian methods; Biomedical imaging; Brain modeling; Clustering algorithms; Gaussian distribution; Head; Image segmentation; Magnetic liquids; Magnetic resonance; Magnetic resonance imaging; Parameter estimation; Shape; Shape measurement; Simulated annealing;
Journal_Title :
Medical Imaging, IEEE Transactions on