Bayesian segmentation of three dimensional images using the EM/MPM algorithm
Abstract
Medical images such as ultrasound, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are typically acquired in three-dimensional (3D) volumes. In addition to true volumetric imaging, sequentially acquired images can be used to form 3D volumes using registration techniques. However, noise and distortion adversely effects clinical interpretation. This is particularly true for medical images such as ultrasound, which have speckle noise caused by reflections and variations in attenuation throughout the tissue structures. A key clinical need is to isolate parts of the 3D volume for interpretation. This requires 3D segmentation to separate tissue types and highlight abnormalities. In practice, very experienced clinicians are needed to accurately diagnose a difficult ultrasound image. Any assistance to this process is beneficial, such as automatic or semi-automatic segmentation. Segmentation using Bayesian techniques on the 2D images are not cohesive when rendered and viewed as volumes. These methods are also not adequate for segmenting the difficult ultrasound cases. Therefore, new 3D Bayesian algorithms are needed. Most 3D Bayesian algorithms find the Maximum a posteriori (MAP) estimate with the iterated conditional mode (ICM) algorithm. This algorithm can be easily trapped in local minima, especially in noisy images. In contrast, the Minimization of Posterior Marginals (MPM) algorithm determines a more appropriate solution in a large range of cases. In addition, the MPM solution provides a robust estimate of the posterior marginal probability used to find an estimate of the Gaussian model statistics used in the Expectation-Maximization (EM) algorithm. In this thesis, a new algorithm is described which extends the combined EM and MPM framework to 3D by including pixels from neighboring frames in the Markov Random Field (MRF) clique. In addition, the adverse attenuation in ultrasound and other medical images is addressed with a new approach that includes a unique linear cost factor introduced in the optimization and a Gaussian posterior distribution with variable mean.
Degree
Ph.D.
Advisors
Delp, Purdue University.
Subject Area
Electrical engineering
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