The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This study aims to analyse the current method in diagnosing early Alzheimer disease and offer a new method to improve the performance of bioinformatics techniques. It proposes a hybrid MRI image processing method to improve the image quality for Alzheimer disease classification. This hybrid method has four stages consisting of image pre-processing, segmentation, feature extraction, and classification...
Skull stripping is an useful technique for segmenting the brain tissue which is used for analysis of neuroimaging data. Thus accurate segmentation of brain tissue by removal of non-brain tissues like skull, muscle/skin, and cerebrospinal fluid is an important task for diagnosis a disease and pre-planning for a surgery. In this paper we present a technique for segmenting the brain from skull in a synthetic...
Discriminating between bipolar disorder (BD) and major depressive disorder (MDD) is a major clinical challenge due to the absence of known biomarkers; hence a better understanding of their pathophysiology and brain alterations is urgently needed. Given the complexity, feature selection is especially important in neuroimaging applications, however, feature dimension and model understanding present...
This paper presents a new data-driven classification pipeline for discriminating two groups of individuals based on the medical images of their brain. The algorithm combines deformation-based morphometry and penalised linear discriminant analysis with resampling. The method is based on sparse representation of the original brain images using deformation logarithms reflecting the differences in the...
Subtraction of ictal and interictal SPECT images is known to be successful in localizing the seizure focus in presurgical evaluation of patients with partial epilepsy. Computer-aided method for producing subtraction ictal SPECT coregistered to MRI (SISCOM method) is commonly used. There are two registrations involved in SISCOM: between the ictal-interictal SPECT images, which was shown to be more...
Larger datasets, with many samples are problematic for solving problems in data mining and machine learning, due to increase in computational times, increased complexity, and bad generalization due to outliers. Further, the accuracy and performance of machine learning and statistical models are still based on tuning of some parameters and optimizing them for generating better predictive models of...
Alzheimer's disease (AD) is the most common progressive neurodegenerative disorder. Therefore, early detection and evaluation of prognosis of AD is an important issue in contemporary brain research. Magnetic Resonance Imaging (MRI) provides valuable diagnostic information about AD. In this work, brain tissue is extracted using phase-based level set method. Structure tensor analysis is used to visualize...
Brain tumor detection and segmentation is one of the most challenging and time consuming task in medical image processing. MRI (Magnetic Resonance Imaging) is a medical technique, mainly used by the radiologist for visualization of internal structure of the human body without any surgery. MRI provides plentiful information about the human soft tissue, which helps in the diagnosis of brain tumour....
Brain structure segmentation from 3D magnetic resonances image (MRI) allows supporting the analysis of physiological and pathological processes. Nonetheless, finding MRI relationships posses a challenge when analyzing in voxel-based high-dimensional spaces. We introduce a kernel-based representation approach to support MRI discrimination. In this sense, inherent Inter-Slice Kernel (ISK) relationship...
Accurate segmentation of brain tumor is very difficult and challenging task due to irregular shape, size, high degree of intensity and textural similarity between normal areas and abnormal regions areas. An intensity based new methodology to segment and detect is proposed which gives more accurate segmentation and detection of abnormal regions from MRI of brain. The accuracy of segmentation methods...
The traditional method for detecting the tumor diseases in the human MRI brain images is done manually by physicians. Automatic classification of tumors of MRI images requires high accuracy, since the non-accurate diagnosis and postponing delivery of the precise diagnosis would lead to increase the prevalence of more serious diseases. To avoid that, an automatic classification system is proposed for...
Problem of non-rigid registration has become very important in the area of biomedical imaging. A non-rigid registration problem is modeled as an optimization problem and is solved using graph cuts and MRFs in recent years. In this paper, we have improved the graph cuts-based solution to non-rigid registration with a novel data term. The proposed data term has several advantages. Firstly, displacement...
In this paper, we describe a new biometric approach based on geometrical characteristics of brain shape. Specifically, we use these geometrics characteristics as a biometric feature to identify individuals. For this purpose, Magnetic Resonance Imaging (MRI) images are considered. We show that using a single slice from an MRI volumetric image, acquired at a given level, one can extract many significant...
With the purpose of providing assistive technology for the communication impaired, we propose a new approach for speech prostheses using vowel speech imagery. Using a hierarchical Bayesian method, electroencephalography (EEG) cortical currents were estimated using EEG signals recorded from three healthy subjects during the performance of three tasks, imaginary speech of vowels /a/ and /u/, and a no...
This paper presents how using a correspondence-based interpolation scheme for 3D image registration improves the registration accuracy. The interpolator takes into account correspondences across slices, which is an advantage, particularly when the volume has thick slices, and where anatomies lie non-parallel to the slice direction. We use our previously presented approach for correspondence-based...
We present the first use of multi-region FDG-PET data for classification of subjects from the Alzheimer's Disease Neuroimaging Initiative. Image data were obtained from 69 healthy controls, 71 AD patients, and 147 patients with a baseline diagnosis of MCI. Anatomical segmentations were automatically generated in the native MRI-space of each subject, and the mean signal intensity per cubic millimetre...
In Multiple Sclerosis (MS) research, it is of interest to evaluate accurately the whole brain atrophy. To address this purpose, a lot of methods are available in the literature. However, no gold standards are available to study the effects of a treatment. We selected a set of 4 methods based on different approaches. A preliminary study allows us to evaluate the accuracy and the reproducibility of...
An estimated 1.4 million Americans suffer from traumatic brain injury (TBI) each year. Current methods of detecting TBI, such as computerized tomography (CT), magnetic resonance imaging (MRI), and Positron Emission Tomography (PET) scanning are time-consuming and expensive. Here, the viability of a potentially more cost-effective means of detecting TBI is presented. Support vector machine (SVM) analyses...
Whole brain extraction is an important pre-processing step in neuro-image analysis. We compared the accuracy of four automated brain extraction methods: Brain Extraction Tool (BET), Brain Surface Extractor (BSE), Hybrid Watershed Algorithm (HWA) and a Multi-Atlas Propagation and Segmentation (MAPS) technique we have previously developed for hippocampal segmentation. The four methods were applied to...
Cortical thickness estimation performed in-vivo via magnetic resonance imaging is an important technique for the diagnosis and understanding of the progression of neurodegenerative diseases. Currently, two different computational paradigms exist, with methods generally classified as either surface or voxel-based. This paper provides a much needed comparison of the surface-based method FreeSurfer and...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.