title: Automatic seed initialization for the expectation-maximization algorithm and its application in 3D medical imaging.
abstract: Statistical partitioning of images into meaningful areas is the goal of all region-based segmentation algorithms. The clustering or creation of these meaningful partitions can be achieved in number of ways but in most cases it is achieved through the minimization or maximization of some function of the image intensity properties. Commonly these optimization schemes are locally convergent, therefore initialization of the parameters of the function plays a very important role in the final solution. In this paper we perform an automatically initialized expectation-maximization algorithm to partition the data in medical MRI images. We present analysis and illustrate results against manual initialization and apply the algorithm to some common medical image processing tasks.
authors: Vision Systems Group, Dublin City University, Dublin 9, Ireland. lynchm@eeng.dcu.ie
authors: M
authors: M
authors: Lynch
authors: D
authors: D
authors: Ilea
authors: K
authors: K
authors: Robinson
authors: O
authors: O
authors: Ghita
authors: P F
authors: PF
authors: Whelan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity