Segmentation and quantitative evaluation of brain MRI data with a multiphase 3D implicit deformable model (original) (raw)

Dynamic segmentation of the cerebral cortex in MR data using implicit active contours

2008

We propose a dynamic coupled-surface level set approach for the segmentation of the cerebral cortex in MR images of the human brain. An iterative scheme for the estimations of local intensity distributions is applied to compensate for artefacts within the data. Results are given for 5 MR data sets acquired on a 3 Tesla Scanner. No pre-processing is required and a constant set of parameters is used for all data sets. We also present a comparison to segmentation results created by the standard neuroimaging software BrainVoyager and show advantages of our approach.

Model-driven, probabilistic level set based segmentation of magnetic resonance images of the brain

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011

Accurate segmentation of magnetic resonance (MR) images of the brain to differentiate features such as soft tissue, tumor, edema and necrosis is critical for both diagnosis and treatment purposes. Region-based formulations of geometric active contour models are popular choices for segmentation of MR and other medical images. Most of the traditional region-based formulations model local region intensity by assuming a piecewise constant approximation. However, the piecewise constant approximation rarely holds true for medical images such as MR images due to the presence of noise and bias field, which invariably results in a poor segmentation of the image. To overcome this problem, we have developed a probabilistic region-based active contour model for automatic segmentation of MR images of the brain. In our approach, a mixture of Gaussian distributions is used to accurately model the arbitrarily shaped local region intensity distribution. Prior spatial information derived from probabi...

A Hybrid Geometric–Statistical Deformable Model for Automated 3-D Segmentation in Brain MRI

IEEE Transactions on Biomedical Engineering, 2000

We present a novel 3-D deformable model-based approach for accurate, robust, and automated tissue segmentation of brain MRI data of single as well as multiple magnetic resonance sequences. The main contribution of this study is that we employ an edge-based geodesic active contour for the segmentation task by integrating both image edge geometry and voxel statistical homogeneity into a novel hybrid geometric-statistical feature to regularize contour convergence and extract complex anatomical structures. We validate the accuracy of the segmentation results on simulated brain MRI scans of both single T1-weighted and multiple T1/T2/PD-weighted sequences. We also demonstrate the robustness of the proposed method when applied to clinical brain MRI scans. When compared to a current state-of-the-art regionbased level-set segmentation formulation, our white matter and gray matter segmentation resulted in significantly higher accuracy levels with a mean improvement in Dice similarity indexes of 8.55% (p < 0.0001) and 10.18% (p < 0.0001), respectively. Index Terms-3-D image segmentation, brain segmentation, deformable models, geodesic active contour.

Segmentation of brain 3D MR images using level sets and dense registration

Medical Image Analysis, 2001

This paper presents a strategy for the segmentation of brain from volumetric MR images which integrates 3D segmentation and 3D registration processes. The segmentation process is based on the level set formalism. A closed 3D surface propagates towards the desired boundaries through the iterative evolution of a 4D implicit function. In this work, the propagation relies on a robust evolution model including adaptive parameters. These depend on the input data and on statistical distribution models. The main contribution of this paper is the use of an automatic registration method to initialize the surface, as an alternative solution to manual initialization. The registration is achieved through a robust multiresolution and multigrid minimization scheme. This coupling significantly improves the quality of the method, since the segmentation is faster, more reliable and fully automatic. Quantitative and qualitative results on both synthetic and real volumetric brain MR images are presented and discussed.

ResearchArticle Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study

2000

This paper presents the implementation and quantitative evaluation of a multiphase three-dimensional deformable model in a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to minimize the sensitivity of the method to initial conditions while avoiding the need for a priori information. This random initialization ensures robustness of the method with respect to the initialization and the minimization set up. Postprocessing corrections with morphological operators were applied to refine the details of the global segmentation method. A clinical study was performed on a database of 10 adult brain MRI volumes to compare the level set segmentation to three other methods: "idealized" intensity thresholding, fuzzy connectedness, and an expectation maximization classification using hidden Markov random fields. Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation computing true positive and false positive volume fractions. A statistical comparison of the segmentation methods was performed through a Wilcoxon analysis of these error rates and results showed very high quality and stability of the multiphase three-dimensional level set method.

3D brain segmentation using active contour with multi labeling method

2012 First National Conference for Engineering Sciences (FNCES 2012), 2012

The main objective task in this paper is 3D multi region segmentation based on active contour with region label prior. The region label prior in our algorithm based on measuring the geometrical different in the brain volume. We will scope on level set with grow-cut method for multi regions segmentation regardless to number of regions in the loaded data. The combining of energy minimization method with grow-cut method is very useful in medical image segmentation to extract the desired object regardless to its grey level. Our algorithm use DICOM medical data and it implement in MATLAB 2008 with the aid of 3D slide viewer to visualize 3D segmentation results. The proposed algorithm shows improvements to segment different region of the brain. Our results compared with the results of other papers that considering the brain segmentation and we found the benefit of our proposed algorithm due to its flexibility to select any object in 2D and 3D DICOM medical images.

Brain MRI segmentation with multiphase minimal partitioning: a comparative study

International journal of biomedical imaging, 2007

Segmentation of brain MRI using active contour model

International Journal of Imaging Systems and Technology

Alzheimer disease is a neurodegenerative disorder that impairs memory, cognitive function, and gradually leads to dementia, physical deterioration, loss of independence, and death of the affected individual. In this context, segmentation of medical images is a very important technique in the field of image analysis and Computer-Assisted Diagnosis. In this article, we introduce a new automatic method of brain images' segmentation based on the Active Contour (AC) model to extract the Hippocampus and the Corpus Callosum (CC). Our contribution is to combine the geometric method with the statistical method of the AC. We used the Caselle Level Set and added a learning phase to build an average shape and to make the initialization task automatic. For the step of contour evolution, we used the principle of Level set and we added to it the a priori knowledge. Experimental results are very promising. V

Segmentation of 3D brain MRI images using active contours and shape priors

2010

The author has further granted permission to Simon Fraser University to keep or make a digital copy for use in its circulating collection (currently available to the public at the "Institutional Repository" link of the SFU Library website <www.lib.sfu.ca> at: http://ir.lib.sfu.ca/handle/1892/112) and, without changing the content, to translate the thesis/project or extended essays, if technically possible, to any medium or format for the purpose of preservation of the digital work. The author has further agreed that permission for multiple copying of this work for scholarly purposes may be granted by either the author or the Dean of Graduate Studies. It is understood that copying or publication of this work for financial gain shall not be allowed without the author's written permission. Permission for public performance, or limited permission for private scholarly use, of any multimedia materials forming part of this work, may have been granted by the author. This information may be found on the separately catalogued multimedia material and in the signed Partial Copyright Licence. While licensing SFU to permit the above uses, the author retains copyright in the thesis, project or extended essays, including the right to change the work for subsequent purposes, including editing and publishing the work in whole or in part, and licensing other parties, as the author may desire.

Level Set Modeling and Segmentation of DT-MRI Brain Data

2003

Segmentation of anatomical regions of the brain is one of the fundamental problems in medical image analysis. It is traditionally solved by iso-surfacing or through the use of active c o n tours/deformable models on a gray-scale MRI data. In this paper we develop a technique that uses anisotropic di usion properties of brain tissue available from DT-MRI to segment out brain structures. We d e v elop a computational pipeline starting from raw di usion tensor data, through computation of invariant anisotropy measures to construction of geometric models of the brain structures. This provides an environment for user-controlled 3D segmentation of DT-MRI datasets. We u s e a l e v el set approach to remove noise from the data and to produce smooth, geometric models. We apply our technique to DT-MRI data of a human subject and build models of the isotropic and strongly anisotropic regions of the brain. Once geometric models have been constructed they may be combined to study spatial relationships and quantitatively analyzed to produce the volume and surface area of the segmented regions.

Segmentation and quantitative evaluation of brain MRI data with a multiphase 3D implicit deformable model (original) (raw)

Related papers