Segmentation of Brain Tissue from Magnetic Resonance Images

Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. Brain tissue is a particularly complex structure, and its segmentation is an important step for studies in temporal change detection of morphology, as well as for 3D visualization in surgical planning. In this paper, we present a method for segmentation of brain tissue from magnetic resonance images that is a combination of three existing techniques from the Computer Vision literature: EM segmentation, binary morphology, and active contour models. Each of these techniques has been customized for the problem of brain tissue segmentation in a way that the resultant method is more robust than its components. Finally, we present the results of a parallel implementation of this method on IBM's supercomputer Power Visualization System for a database of 20 brain scans each with 256x256x124 voxels and validate those against segmentations generated by neuroanatomy experts

Brain Tissues Segmentation on MR Perfusion Images Using CUSUM Filter for Boundary Pixels

The fully automated and relatively accurate method of brain tissues segmentation on T2-weighted magnetic resonance perfusion images is proposed. Segmentation with this method provides a possibility to obtain perfusion region of interest on images with abnormal brain anatomy that is very important for perfusion analysis. In the proposed method the result is presented as a binary mask, which marks two regions: brain tissues pixels with unity values and skull, extracranial soft tissue and background pixels with zero values. The binary mask is produced based on the location of boundary between two studied regions. Each boundary point is detected with CUSUM filter as a change point for iteratively accumulated points at time of moving on a sinusoidal-like path along the boundary from one region to another. The evaluation results for 20 clinical cases showed that proposed segmentation method could significantly reduce the time and efforts required to obtain desirable results for perfusion region of interest detection on T2-weighted magnetic resonance perfusion images with abnormal brain anatomy

Multispectral segmentation of whole-brain MRI

Magnetic Resonance Imaging (MRI) is a widely used medical technology for diagnosis and detection of various tissue abnormalities, tumor detection, and in evaluation of either residual or recurrent tumors. This thesis work exploits MRI information acquired on brain tumor structure and physiological properties and uses a novel image segmentation technique to better delineate tissue differences.;MR image segmentation will be important in distinguishing between boundaries of different tissues in the brain. A segmentation software tool was developed that combines the different types of clinical MR images and presents them as a single colored image. This technique is based on the fuzzy c-means (FCM) clustering algorithm. The MR data sets are used to form five-dimensional feature vectors. These vectors are segmented by FCM into six tissue classes for normal brains and nine tissue classes for human brains with tumors. The segmented images are then compared with segmentation performed using Statistical Parametric Mapping (SPM2)---software that is commonly used for brain tissue segmentation. The results from segmenting the whole volume MRI using FCM show better distinction between tumor tissues than SPM2

Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm

An important area of current research is obtaining more information about brain structure and function. Brain tissue is particularly complex structure and its segmentation is an important step for studies intemporal change, detection of morphology as well as visualization in surgical planning, volume estimation of objects of interest, and more could benefit enormously from segmentation. Magnetic resonance imaging (MRI) is a noninvasive method for producing tomographic images of the human brain. Its Segmentation is problematic due to radio frequency inhomogeneity, caused by inaccuracies in the magnetic resonance scanner and by movement of the patient which produce intensity variation over the image, and that makes every segmentation method fail. The aim of this work is the development of a segmentation technique for efficient and accurate segmentation of MR brain images. The proposed technique based on the watershed algorithm, which is applied to the gradient magnitude of the MRI data. The watershed segmentation algorithm is a very powerful segmentation tool, but it also has difficulty in segmenting MR images due to noise and shading effect present. The known drawback of the watershed algorithm, over-segmentation, is strongly reduced by making the system interactive (semi-automatic), by placing markers manually in the region of interest which is the brain as well as in the background. The background markers are needed to define the external contours of the brain. The final part of the segmentation takes place once the gradient magnitudes of the MRI data are calculated and markers have been obtained from each region. Catchment’s basins originate from each of the markers, resulting in a common line of separation between brain and surrounding. The proposed segmentation technique is tested and evaluated on brain images taken from brainweb. Brainweb is maintained by the Brain Imaging Center at the Montreal Neurological Institute. The images had a combination of noise and intensity non-uniformity (INU). By making the system semi-automatic, a good segmentation result was obtained under all the conditions (different noise levels and intensity non uniformity). It is also proven that the placement of internal and external markers into regions of interest (i.e. making the system interactive) can easily cope with the over-segmentation problem of the watershed

A Survey of Brain Tumour Segmentation Methods- A Review

Brain tumor segmentation aims to separate the different tumor tissues such as active cells, necrotic core, and edema from normal brain tissues of White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). MRI based brain tumor segmentation studies are attracting more and more attention in recent years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging (MRI) images. With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting brain tumo rare becoming more and more mature and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview of MRI-based brain tumor segmentation methods

Segmentation of brain MRI during early childhood

The objective of this thesis is the development of automatic methods to measure the changes in volume and growth of brain structures in prematurely born infants. Automatic tools for accurate tissue quantification from magnetic resonance images can provide means for understanding how the neurodevelopmental effects of the premature birth, such as cognitive, neurological or behavioural impairment, are related to underlying changes in brain anatomy. Understanding these changes forms a basis for development of suitable treatments to improve the outcomes of premature birth. In this thesis we focus on the segmentation of brain structures from magnetic resonance images during early childhood. Most of the current brain segmentation techniques have been focused on the segmentation of adult or neonatal brains. As a result of rapid development, the brain anatomy during early childhood differs from anatomy of both adult and neonatal brains and therefore requires adaptations of available techniques to produce good results. To address the issue of anatomical differences of the brain during early childhood compared to other age-groups, population-specific deformable and probabilistic atlases are introduced. A method for generation of population-specific prior information in form of a probabilistic atlas is proposed and used to enhance existing segmentation algorithms. The evaluation of registration-based and intensity-based approaches shows the techniques to be complementary in the quality of automatic segmentation in different parts of the brain. We propose a novel robust segmentation method combining the advantages of both approaches. The method is based on multiple label propagation using B-spline non-rigid registration followed by EM segmentation. Intensity inhomogeneity is a shading artefact resulting from the acquisition process, which significantly affects modern high resolution MR data acquired at higher magnetic field strengths. A novel template based method focused on correcting the intensity inhomogeneity in data acquired at higher magnetic field strengths is therefore proposed. The proposed segmentation method combined with proposed intensity inhomogeneity correction method offers a robust tool for quantification of volumes and growth of brain structures during early childhood. The tool have been applied to 67 T1-weigted images of subject at one and two years of age

Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described

Use of MRI to measure whole brain atrophy in MS patients

Nowadays magnetic resonance imaging is used for anatomical assessment of human brain structures in neurodegenerative disorders causing brain atrophy for instance in multiple sclerosis (MS) or in Alzheimer Disease. Pathological brain tissue loss can be described in terms of change in the brain parenchymal fraction (BPF). This work shows the impact of segmentation method in SPM12 and additional segmentation in Computational Anatomy Toolbox (CAT12) on calculated BPF value for patients suffer from with MS no treated and treated with disease-modifying drug (DMD) interferon-beta (INFb) for one year and two years. Both methods confirm that brain parenchymal fraction decreases with age, nevertheless for patients not treated INFb decreases faster than for treated. An usability of Lesion Segmentation Tool toolbox in process of automatic detection and segmentation T2 hyperintense lesions in FLAIR images is discussed[…