2,227 research outputs found
Visual and Contextual Modeling for the Detection of Repeated Mild Traumatic Brain Injury.
Currently, there is a lack of computational methods for the evaluation of mild traumatic brain injury (mTBI) from magnetic resonance imaging (MRI). Further, the development of automated analyses has been hindered by the subtle nature of mTBI abnormalities, which appear as low contrast MR regions. This paper proposes an approach that is able to detect mTBI lesions by combining both the high-level context and low-level visual information. The contextual model estimates the progression of the disease using subject information, such as the time since injury and the knowledge about the location of mTBI. The visual model utilizes texture features in MRI along with a probabilistic support vector machine to maximize the discrimination in unimodal MR images. These two models are fused to obtain a final estimate of the locations of the mTBI lesion. The models are tested using a novel rodent model of repeated mTBI dataset. The experimental results demonstrate that the fusion of both contextual and visual textural features outperforms other state-of-the-art approaches. Clinically, our approach has the potential to benefit both clinicians by speeding diagnosis and patients by improving clinical care
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Automatic classification of MR brain tumor images using KNN, ANN, SVM and CNN
A brain tumor classification system has been designed and developed. This work presents a new approach to the automated classification of astrocytoma, medulloblastoma, glioma, glioblastoma multiforme and craniopharyngioma type of brain tumors based on first order statistics and gray level co-occurrence matrix, in magnetic resonance images. The magnetic resonance feature image used for the tumor detection consists of T2-weighted magnetic resonance images for each axial slice through the head. To remove the unwanted noises in the magnetic resonance image, median filtering is used. First order statistics and gray level co-occurrence matrix-based features are extracted. Finally, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks are used to classify the brain tumor images. The application of the proposed method for tracking tumor is demonÂstrated to help pathologists distinguish its type of tumor. A classification with an accuracy of 89%, 90%, 91% and 95% has been obtained by, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks
Segmentation of Infant Brain Using Nonnegative Matrix Factorization
This study develops an atlas-based automated framework for segmenting infants\u27 brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant\u27s brain at the isointense age (6-12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov-Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix factorization (NMF) and allocation to data clusters. Projecting an initial high-dimensional feature space onto a low-dimensional space of the significant fused features with the NMF allows for better separation of the goal structure and its background. The cluster centers in the latter space are determined at the training stage by the K-means clustering. In order to adapt to large infant brain inhomogeneities and segment the brain images more accurately, appearance descriptors of both the first-order and second-order are taken into account in the fused NMF feature space. Additionally, a second-order MGRF model is used to describe the appearance based on the voxel intensities and their pairwise spatial dependencies. An adaptive shape prior that is spatially variant is constructed from a training set of co-aligned images, forming an atlas database. Moreover, the spatial homogeneity of the shape is described with a spatially uniform 3D MGRF of the second-order for region labels. In vivo experiments on nine infant datasets showed promising results in terms of the accuracy, which was computed using three metrics: the 95-percentile modified Hausdorff distance (MHD), the Dice similarity coefficient (DSC), and the absolute volume difference (AVD). Both the quantitative and visual assessments confirm that integrating the proposed NMF-fused DTI feature and intensity MGRF models of visual appearance, the adaptive shape prior, and the shape homogeneity MGRF model is promising in segmenting the infant brain DTI
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Brain Tumor Detection and Segmentation in Multisequence MRI
Tato práce se zabĂ˝vá detekcĂ a segmentacĂ mozkovĂ©ho nádoru v multisekvenÄŤnĂch MR obrazech se zaměřenĂm na gliomy vysokĂ©ho a nĂzkĂ©ho stupnÄ› malignity. Jsou zde pro tento účel navrĹľeny tĹ™i metody. PrvnĂ metoda se zabĂ˝vá detekcĂ prezence částĂ mozkovĂ©ho nádoru v axiálnĂch a koronárnĂch Ĺ™ezech. Jedná se o algoritmus zaloĹľenĂ˝ na analĂ˝ze symetrie pĹ™i rĹŻznĂ˝ch rozlišenĂch obrazu, kterĂ˝ byl otestován na T1, T2, T1C a FLAIR obrazech. Druhá metoda se zabĂ˝vá extrakcĂ oblasti celĂ©ho mozkovĂ©ho nádoru, zahrnujĂcĂ oblast jádra tumoru a edĂ©mu, ve FLAIR a T2 obrazech. Metoda je schopna extrahovat mozkovĂ˝ nádor z 2D i 3D obrazĹŻ. Je zde opÄ›t vyuĹľita analĂ˝za symetrie, která je následována automatickĂ˝m stanovenĂm intenzitnĂho prahu z nejvĂce asymetrickĂ˝ch částĂ. TĹ™etĂ metoda je zaloĹľena na predikci lokálnĂ struktury a je schopna segmentovat celou oblast nádoru, jeho jádro i jeho aktivnà část. Metoda vyuĹľĂvá faktu, Ĺľe vÄ›tšina lĂ©kaĹ™skĂ˝ch obrazĹŻ vykazuje vysokou podobnost intenzit sousednĂch pixelĹŻ a silnou korelaci mezi intenzitami v rĹŻznĂ˝ch obrazovĂ˝ch modalitách. JednĂm ze zpĹŻsobĹŻ, jak s touto korelacĂ pracovat a pouĹľĂvat ji, je vyuĹľitĂ lokálnĂch obrazovĂ˝ch polĂ. Podobná korelace existuje takĂ© mezi sousednĂmi pixely v anotaci obrazu. Tento pĹ™Ăznak byl vyuĹľit v predikci lokálnĂ struktury pĹ™i lokálnĂ anotaci polĂ. Jako klasifikaÄŤnĂ algoritmus je v tĂ©to metodÄ› pouĹľita konvoluÄŤnĂ neuronová sĂĹĄ vzhledem k jejĂ známe schopnosti zacházet s korelacĂ mezi pĹ™Ăznaky. Všechny tĹ™i metody byly otestovány na veĹ™ejnĂ© databázi 254 multisekvenÄŤnĂch MR obrazech a byla dosáhnuta pĹ™esnost srovnatelná s nejmodernÄ›jšĂmi metodami v mnohem kratšĂm vĂ˝poÄŤetnĂm ÄŤase (v řádu sekund pĹ™i pouĹľitĂ˝ CPU), coĹľ poskytuje moĹľnost manuálnĂch Ăşprav pĹ™i interaktivnĂ segmetaci.This work deals with the brain tumor detection and segmentation in multisequence MR images with particular focus on high- and low-grade gliomas. Three methods are propose for this purpose. The first method deals with the presence detection of brain tumor structures in axial and coronal slices. This method is based on multi-resolution symmetry analysis and it was tested for T1, T2, T1C and FLAIR images. The second method deals with extraction of the whole brain tumor region, including tumor core and edema, in FLAIR and T2 images and is suitable to extract the whole brain tumor region from both 2D and 3D. It also uses the symmetry analysis approach which is followed by automatic determination of the intensity threshold from the most asymmetric parts. The third method is based on local structure prediction and it is able to segment the whole tumor region as well as tumor core and active tumor. This method takes the advantage of a fact that most medical images feature a high similarity in intensities of nearby pixels and a strong correlation of intensity profiles across different image modalities. One way of dealing with -- and even exploiting -- this correlation is the use of local image patches. In the same way, there is a high correlation between nearby labels in image annotation, a feature that has been used in the ``local structure prediction'' of local label patches. Convolutional neural network is chosen as a learning algorithm, as it is known to be suited for dealing with correlation between features. All three methods were evaluated on a public data set of 254 multisequence MR volumes being able to reach comparable results to state-of-the-art methods in much shorter computing time (order of seconds running on CPU) providing means, for example, to do online updates when aiming at an interactive segmentation.
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