Voting-based active contour segmentation of fMRI images of the brain (original) (raw)
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Multiphase Region-based Active Contours for Semi-automatic Segmentation of Brain MRI Images
Proceedings of the 10th International Conference on Computer Vision Theory and Applications, 2015
Segmenting brain magnetic resonance (MRI) images of the brain into white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) is an important problem in medical image analysis. The study of these regions can be useful for determining different brain disorders, assisting brain surgery, post-surgical analysis, saliency detection and for studying regions of interest. This paper presents a segmentation method that partitions a given brain MRI image into WM, GM and CSF regions through a multiphase region-based active contour method followed by a pixel correction thresholding stage. The proposed region-based active contour method is applied in order to partition the input image into four different regions. Three of those regions within the brain area are then chosen by intersecting a hand-drawn binary mask with the computed contours. Finally, an efficient thresholding-based pixel correction method is applied to the computed WM, GM and CSF regions to increase their accuracy. The segmentation results are compared with ground truths to show the performance of the proposed method.
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
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.
Fast and globally convex multiphase active contours for brain MRI segmentation
Computer Vision and Image Understanding, 2014
Multiphase active contour based models are useful in identifying multiple regions with different characteristics such as the mean values of regions. This is relevant in brain magnetic resonance images (MRIs), allowing the differentiation of white matter against gray matter. We consider a well defined globally convex formulation of Vese and Chan multiphase active contour model for segmenting brain MRI images. A well-established theory and an efficient dual minimization scheme are thoroughly described which guarantees optimal solutions and provides stable segmentations. Moreover, under the dual minimization implementation our model perfectly describes disjoint regions by avoiding local minima solutions. Experimental results indicate that the proposed approach provides better accuracy than other related multiphase active contour algorithms even under severe noise, intensity inhomogeneities, and partial volume effects.
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.
Segmentation of brain tissue from magnetic resonance images
Medical Image Analysis, 1996
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.
MR Brain Image Segmentation Using Region Based Active Contour Model
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Various image segmentation methods are widely used for finding diseases and illness. Detection of any kind of brain tumors from magnetic resonance imaging (MRI) is very important for radiologists and image processing researchers. This paper described a segmentation method based on region based active contour model using level set approach to be useful for region of interest (ROI) based image compression system. The brain tumors (ROI) may be anywhere in MR brain images. The aim of this paper is to segment an image into non-intersecting regions, region of interest and other than region of interest and background for region based medical image compression system. In this system, the initial mask is firstly created. The initial curve can be anywhere in the images and interior contours are automatically detected. This method performs two main steps, curve evolution and segmenting process. Curve evolution is done by using level set method and active contour model segments the region. The ...
Segmentation of 3D brain MRI images using active contours and shape priors
2010
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Segmentation of brain tissue from magnetic resonance image
IEEE Transactions on Medical Imaging - TMI, 1997
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.
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies - ISABEL '11, 2011
This paper presents a new method for segmenting multiple brain structures by using an optimized mixture of different Active Contour Models (ACMs). Prior constraints and structures' neighboring interaction are modelled for each structure. Prior information is also captured by a training process, in which structure's dependent local and global weights are calculated. The local weights regulate locally the combination of each term during the evolution, acting as an experienced balancer between image and prior information. The ideal proportion of relation between the mixture of different ACMs and the prior model is defined by the optimum global weights. As proof of concept, the method is applied on the very challenging task of segmenting hippocampus and amygdala structures.