Automatic And Three-dimensional Tissue Segmentation Of Brain MRI

In the last two decades, the field of medical image analysis has greatly influenced many areas in neuroscience. With the advancement of the medical imaging technologies, neuroscientists have been increasingly interested in methodologies that can identify brain normal tissues, subcortical structures and pathological tissues in anatomical imaging modalities. Many neuroscience studies aim to find new disease related anatomical characteristics in order to increase the reliability of diagnosing the illness or improving the effectiveness of treatment methods against the brain disease. As the one of the most important branches of segmentation of image, the segmentation of medical image is the primary and critical step for the analyzing and understanding the medical images.Today, with the advancement of the Magnetic Resonance Imaging (MRI), the MRI has provided a means for imaging tissues in the brain at very high contrast and resolution in the three dimensional space. Most neuroscientists are keenly interested in outlining the three main brain "tissue" classes - cerebral spinal fluid (CSF), white matter (WM) and gray matter (GM) - in Magnetic Resonance (MR) images and further parcellating these tissue classes into their substructures such as cerebral ventricles, thalamus and so on. These anatomical studies of brain tissues and structures with disease are often based on the analysis of Magnetic Resonance images. The segmentation of medical images provides different kinds of algorithms or tools to segment and extract the tissues and structures in the brain automatically or interactively from MR images for the analysis.The task of automatically segmenting medical images is challenging as the images are corrupted by several artifacts. Because of the limited resolution and imperfection of the medical imaging devices, the sampled MR images from clinic are often degraded by noise, bias field (BF, also known as intensity non-uniformity, INU), partial volume effects (PVE) and motive artifacts. In addition, the complex shape, boundary and topology of brain tissues and structures make the accurate, fast and robust segmentation of brain tissues very difficult. Furthermore, two-dimensional (2D) segmentation of medical images can not meet the demands of clinic and research anymore. Three-dimensional (3D) segmentation and visualization of medical images becomes popular which is because of the 3D nature of the tissues and structures of brain in reality. Moreover, the 3D segmentation offers more accurate and continuous results utilizing the more rich information provided by 3D medical imaging data volumes as much as possible. The visualization of objective structures is more vivid with rich relevant 3D information about shape, size and location.By far, the field of medical image analysis has developed a variety of automatic segmentation methods to fulfill the difficult problem of medical segmentation. However, the current medical segmentation algorithms still can't satisfy the various demands in clinic and research completely. The reasons causing the above deficient state of the field include poor mathematic model for describing the problem faced in practice, significant difference between different targets to be segmented, degraded medical images due to the imperfectness of the imaging devices, random and complex change of pathological tissue, diverse expectances of the result, and so on. So, there is no such an algorithm of segmentation which is competent for all kinds of the problems. What we can do is to develop different algorithms of segmentation for different problems.In this thesis, the current algorithms of medical image segmentation are reviewed in detail especially the 3D segmentation algorithms for brain tissues. Some new models and algorithms are proposed to fulfill the accurate and fast 3D segmentation of brain tissues and structures on the following three levels. As researchers have been furthering the studies in brain and the related diseases, the segmentation of brain tissues has been stratified into three levels: the segmentation of three main tissues of brain, the segmentation of the subcortical structures and the segmentation of pathological tissues. We believe that the three levels are interdependent in automatic parcellation of brain. In the thesis, the following creative research works are included:First, an new intensity based improved FCM algorithm is presented to fast segment the MR brain volumes with significant bias field and noise into three main brain tissues. The algorithm is formulated by proposing a new objective function based on standard FCM algorithm with bias field correction and neighborhood constrain. In the algorithm, a parameterized model is adopted to express the bias field and a neighbor constrain on membership vectors similar to Markov random field (MRF) is proposed to express spatial consistency of brain tissue. The proposed algorithm segment MR data volumes and estimate the bias field without need for a logarithmic transformation and preprocessing. Experimental results with both synthetic and real clinic data are included, as well as comparisons of the performance of our algorithm with that of other published methods. The validation of the algorithm shows good accuracy and fast convergence. Second, in the paper, a new multi-constrain and dynamic prior based automatic 3D Segmentation algorithm called MCDPMRF-EM is developed for segmenting MR Brain volumes into three main brain tissues. A novel big scale constrain extracted from MR volumes is introduced into Markov Random Field model (MRF) in the algorithm. The algorithm searches the optimal segmentation configuration using the Maximum a Posteriori (MAP) criteria and a modified expectation-maximization algorithm (EM) in the Bayesian frame. The MCDPMRF-EM algorithm segments MR brain volumes corrupted by bias field and noise accurately and robustly. The results of the algorithm are more consistent with the known anatomical facts. The proposed algorithm incorporates the following novel features.1)The multi-constrain model is proposed as well as its creation to improve the function of statistical segmentation algorithms. By incorporating the new model, our algorithm is insensitive to class number, and the segmentation result of our algorithm is more consistent with the known anatomical facts.2)The dynamic prior concept is also proposed to simulate the self adaptive function of human eyes. The dynamic prior can automatic adjust the constrain to effectively conquer the bias field.3)We propose parametric, smooth models for the intensity of each class instead of multiplicative bias field that affects tissue intensities. This may be a more realistic model and avoids the need for a logarithmic transformation and, hence, the related nonlinear distortions.4) We propose a novel variant of the EM algorithm which allows for the use of a fast and accurate way to find optimal segmentations, given the intensity models which incorporate MRF spatial coherence assumptions.Third, the MRI-based quantity analysis of substantia nigra (SN) in human brain has more and more value in diagnosis of Parkinson disease in today. We describe an anatomic knowledge-constrained algorithm based on active surface model and adaptive region growth to automatically delineate the SN region from a magnetic resonance image. The result of the algorithm can be used to calculate position, shape and volume and help early clinical diagnosis as well as treating effect of SN. The validation of the algorithm was tested and showed good accuracy and adaptation.Fourth, we propose a graph-based three-dimensional (3D) algorithm to automatically segment brain tumors from magnetic resonance images (MRI). The algorithm uses minimum s/t cut criteria to obtain a global optimal result of objective function formed according to Markov Random Field Model and Maximum a posteriori (MAP-MRF) theory, and by combining the expectation-maximization (EM) algorithm to estimate the parameters of mixed Gaussian model for normal brain and tumor tissues. 3D segmentation results of brain tumors are fast achieved by our algorithm. The validation of the algorithm was tested and showed good accuracy and adaptation under simple interactions with the physicians. Last, the human cerebral ventricular system consists of four inter-communicating chambers. Changes in CSF volume and ventricular shape are associated with several neurological diseases. Quantification of the degree of abnormal enlargement of ventricles is important in diagnosis of various diseases, measuring the response to treatment, and predicting the prognosis of the disease process. In this paper, we present a novel 3D hybrid approach for automatic extraction of human cerebral ventricular system from MR neuroimages. The approach consists of following two algorithms and intermediate step serially. First, an new 3D algorithm of segmentation called MCDPMRF-EM based on multi-constrain and dynamic prior with Markov Random Field model, which optimized by a Maximum A posteriori Probability criteria and the expectation maximization algorithm. The algorithm segment brain tissues into five tissue class types and estimate bias field (BF) accurately and robustly from MR image volumes. Second, an intermediate step of some morphological processes is followed with the above algorithm to extract the seed regions which are the parts of the four ventricles. Thirdly, we use a combinatorial s/t graph cuts algorithm with the hard constraints to segment the ventricular system from MR neuroimages. Our approach can automatically extract the complete ventricular system with no need for denoise and correction of bias field. The test result of the approach is accurate and agrees with the anatomical structure of the ventricular system.