Research On Brain MR Images Segmentation

Accurate segmentation of magnetic resonance (MR) images of the brain is undoubtedly of great interest for the study and the treatment of various pathologies such as Alzheimer dis-ease, Parkinson or Parkinson related syndrome. In fact, intensity inhomogeneity often occurs in MR images due to radio frequency (RF) coils or acquisition sequences. The intensity inho-mogeneity in MR images often appears as an intensity variation across the image. Thus the resultant intensities of the same tissue vary with the locations in the image. Although usually hardly noticeable to a human observer, such a bias can cause serious misclassifications when intensity-based segmentation algorithms are used. In addition, images are often corrupted by various noises that challenge the segmentation. Besides these difficulties, the boundaries be-tween tissues are quite fuzzy due to the partial volume (PV) effect. Compared with the adult brain MR images, contrast in neonatal MR images is much lower than that of adult because the majority of white matter is as-yet unmyelinated and has a water content closer to that of gray matter than in adults and adolescents. Besides the image contrast, the intensities of tissues are significantly affected by intensity inhomogeneity due to not only RF inhomogeneity but also biological properties of the developing tissue, which leads to a large overlap in their intensity distributions. The inversion of contrast between GM and WM, compared to adult MRI, is also a difficulty given the limited resolution of neonate MRI. Due to this inverted GM/WM con-trast, many voxels between CSF and GM can be incorrectly classified as WM by conventional intensity-based segmentation approaches. In a word, it remains challenging to segment brain MR images, especially for neonatal brain images. In this paper, we first propose a few segmen-tation methods based on active contour model to deal with the intensity inhomogeneity. These proposed active contour models are then intergraded into a convex framework to simultaneously segment the brain MR images and correct bias fields. The convexity of the framework makes the model robust to initialization. We further extend the convex framework and active con- tour model proposed previously into neonatal brain MR image segmentation. Our work mainly includes the following parts:(1) To overcome the difficulty caused by intensity inhomogeneity in the segmentation of magnetic resonance (MR) images, this paper presents a new multiphase level set method for segmentation of brain MR images. The proposed model utilizes local image intensities, which enables it to cope with intensity inhomogeneity. With the multiphase level set framework, the model can extract brain white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) simultaneously and provide a smooth contour/surface. Comparisons of 2D and 3D segmentation prove that the proposed model is effective.(2) We propose an improved region-based active contour model in a variational level set formulation. We define an energy functional with a local intensity fitting term, which induces a local force to attract the contour and stops it at object boundaries, and an auxiliary global intensity fitting term, which drives the motion of the contour far away from object boundaries. Therefore, the combination of these two forces allows for flexible initialization of the contours. The proposed model is first presented as a two-phase level set formulation and then extended to a multi-phase formulation. The proposed method has been applied to brain MR image segmen-tation with desirable results.(3) We propose a new region-based active contour model in a variational level set for-mulation for image segmentation. In our model, the local image intensities are described by Gaussian distributions with different means and variances. We define a local Gaussian distri-bution fitting energy with a level set function and local means and variances as variables. The means and variances of local intensities are considered as spatially varying functions to handle intensity inhomogeneities. It is worth noting that the local intensity means and variances, which are two variables of the proposed energy functional, are strictly derived from a variational prin-ciple, instead of being defined empirically. Comparisons with classic methods demonstrate the advantages of the proposed method in terms of accuracy. (4) We propose a novel method to simultaneously segment the brain MR images and cor-rect the bias fields. The proposed energy is multiphase and convex with respect to its partition variables. Therefore, our method is very robust to the initializations. We also propose a fast and accurate minimization algorithm based on the Split Bregman method for our energy. The flexibility of the method is shown with 2D and 3D segmentation examples of brain MR images. Moreover, we propose a novel convex framework utilizing local image statistical information for simultaneously segmentation of the brain MR images and correction of the bias fields. Many models can be seen as the special cases of the proposed framework. Comparisons also demon-strate the advantages of the proposed convex framework.(5) We present a novel surface-based method for neonatal brain segmentation. Our method effectively utilizes local image information, atlas prior knowledge, and cortical thickness con-straint for guiding the segmentation, by integrating them into a coupled level set method. We also provide a robust initialization method using convex optimization for this coupled level set method. Our proposed method has been validated on 10 subjects with promising results.