Segmentation Of MRI Images Using Non-parametric Deformable Models Integrating Fuzzy Technique

The research goal in this dissertation is to develop an automatic segmentation method to segment brain MRI images into different tissue classes (gray matter, white matter, and cerebrospinal fluid), to provide quantitative brain measurements to the study of brain development and human aging, disease diagnosis and treatment, surgical planning and navigation, and other applications. In this dissertation, we develop several algorithms which integrate the non-parametric deformable models with statistical information or fuzzy information of images to segment the brain MRI images. These algorithms are assessed and validated with the experiments on multi-modalities of MRI images:T1-weighted, T2-weighted and PD-weighted.We firstly present a histogram-analysis based non-parametric deformable model, where the intensity histogram of the MRI image is modeled via the mixture Gaussian model (MGM). The parameters of each component in MGM are estimated via the Expectation Maximization (EM) algorithm. Then the estimated parameters are used to generate the constraint term to guide the evolution of the level set curves to achieve the brain tissues segmentation. The algorithm is evaluated with the simulated and real MRI images. The segmentation results and quantitative analyses are provided.We then explore the region-based geometric active contour (RGAC) of Suri. Based on the stability analysis, we propose the improved algorithm of the RGAC with new regional force terms. The new algorithm solves the underlying stability problem associated with the original algorithm. Compared with the original algorithm, the improved algorithm achieves convergence with less iteration number, and its segmentation results are less sensitive to some parameters. The algorithm can segment brain tissues into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) classes from different modalities of MRI images (T1-weighted, T2-weighted, and PD-weighted). The algorithm is evaluated with ten simulated MRI images and five real MRI datasets, and compared quantitatively with other algorithms. The proposed fuzzy region indicator is used in an adaptive level set method. The method adaptively adjusts the directions of fronts during the curves evolution and achieves the final segmentation with fast convergence rate. The algorithm overcomes the limitations, associated with geometric active contour, of overly relying on the gradient information of images and reducing in accuracy of boundary locating caused by Gaussian smoothing. The algorithm is evaluated with the experiments on simulated and real MRI images.Finally, we present a multiclass algorithm by integrating fuzzy clustering analysis with the level set methods. The algorithm uses a set of ordinary differential equations; each of them represents a class to be segmented. The algorithm consists of the anisotropic diffusion filtering, fuzzy clustering analysis and the level set refining segmentation steps. The algorithm is also evaluated with synthetic images,20 simulated MRI images and 10 real MRI datasets. Compared with the multiphase algorithm, the multiclass algorithm reduces the computational complexity, can achieve faster convergence. The comparison with other algorithms indicates the better segmentation performance and good robustness to noises.The adopted fuzzy logic framework in the proposed algorithms allows for the ambiguity and uncertainty of the brain tissues in MRI images, can retain more information than the crisp approaches. The combination of fuzzy C-means (FCM) with level set methods allows to improve the segmentation performance and robustness of the algorithms.