Nooshin Nabizadeh

Brain tumors detection and segmentation in MR images

Display Omitted A fully automatic system for detection of slices that contain tumor in MR images is presented.A fully automatic system for tumor segmentation using single-spectral MR images is presented.A study for evaluating the efficacy of statistical features over Gabor wavelet features is included. Automated recognition of brain tumors in magnetic resonance images (MRI) is a difficult procedure owing to the variability and complexity of the location, size, shape, and texture of these lesions. Because of intensity similarities between brain lesions and normal tissues, some approaches make use of multi-spectral anatomical MRI scans. However, the time and cost restrictions for collecting multi-spectral MRI scans and some other difficulties necessitate developing an approach that can detect tumor tissues using a single-spectral anatomical MRI images. In this paper, we present a fully automatic system, which is able to detect slices that include tumor and, to delineate the tumor area. The experimental results on single contrast mechanism demonstrate the efficacy of our proposed technique in successfully segmenting brain tumor tissues with high accuracy and low computational complexity. Moreover, we include a study evaluating the efficacy of statistical features over Gabor wavelet features using several classifiers. This contribution fills the gap in the literature, as is the first to compare these sets of features for tumor segmentation applications.

Cognitive correlates of white matter lesion load and brain atrophy: The Northern Manhattan Study

Objective: We investigated white matter lesion load and global and regional brain volumes in relation to domain-specific cognitive performance in the stroke-free Northern Manhattan Study (NOMAS) population. Methods: We quantified white matter hyperintensity volume (WMHV), total cerebral volume (TCV), and total lateral ventricular (TLV) volume, as well as hippocampal and cortical gray matter (GM) lobar volumes in a subgroup. We used general linear models to examine MRI markers in relation to domain-specific cognitive performance, adjusting for key covariates. Results: MRI and cognitive data were available for 1,163 participants (mean age 70 ± 9 years; 60% women; 66% Hispanic, 17% black, 15% white). Across the entire sample, those with greater WMHV had worse processing speed. Those with larger TLV volume did worse on episodic memory, processing speed, and semantic memory tasks, and TCV did not explain domain-specific variability in cognitive performance independent of other measures. Age was an effect modifier, and stratified analysis showed that TCV and WMHV explained variability in some domains above age 70. Smaller hippocampal volume was associated with worse performance across domains, even after adjusting for APOE ε4 and vascular risk factors, whereas smaller frontal lobe volumes were only associated with worse executive function. Conclusions: In this racially/ethnically diverse, community-based sample, white matter lesion load was inversely associated with cognitive performance, independent of brain atrophy. Lateral ventricular, hippocampal, and lobar GM volumes explained domain-specific variability in cognitive performance.

Histogram-based gravitational optimization algorithm on single MR modality for automatic brain lesion detection and segmentation

Magnetic resonance imaging (MRI) is a very effective medical imaging technique for the clinical diagnosis and monitoring of neurological disorders. Because of intensity similarities between brain lesions and normal tissues, multispectral MRI modalities are usually applied for brain lesion detection. However, the time and cost restrictions for collecting multi-spectral MRI, and the issue of possible errors from registering multiple MR images necessitate developing an automatic lesion detection approach that can detect lesions using a single anatomical MRI modality. In this paper, an automatic algorithm for brain stroke and tumor lesion detection and segmentation using single-spectral MRI is presented. The proposed algorithm, called histogram-based gravitational optimization algorithm (HGOA), is a novel intensity-based segmentation technique, which applies enhanced gravitational optimization algorithm on histogram analysis results. The mathematical descriptions as well as the convergence criteria of the developed optimization algorithm are presented in detail. Using this algorithm, brain is segmented into different number of regions, which will be labeled as lesion or healthy. Here, the ischemic stroke lesions and tumor lesions are segmented with 91.5% and 88.1% accuracy, respectively.

Automatic tumor segmentation in single-spectral MRI using a texture-based and contour-based algorithm

We propose a new fully automatic method to detect and segment brain lesions.The method is based on a texture-based and a contour-based algorithm.The algorithm is independent of multi-spectral MRI, and local or global registration. Automatic detection of brain tumors in single-spectral magnetic resonance images is a challenging task. Existing techniques suffer from inadequate performance, dependence on initial assumptions, and, sometimes, the need for manual interference. The research reported in this paper seeks to reduce some of these shortcomings, and to remove others, achieving satisfactory performance at reasonable computational costs. The success of the system described here is explained by the synergy of the following aspects: (1) a broad choice of high-level features to characterize the images texture, (2) an efficient mechanism to eliminate less useful features (3) a machine-learning technique to induce a classifier that signals the presence of a tumor-affected tissue, and (4) an improved version of the skippy greedy snake algorithm to outline the tumors contours. The paper describes the system and reports experiments with synthetic as well as real data.

Drug target ontology to classify and integrate drug discovery data

BackgroundOne of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome.ResultsAs part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships.ConclusionsDTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/, Github (http://github.com/DrugTargetOntology/DTO), and the NCBO Bioportal (http://bioportal.bioontology.org/ontologies/DTO). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Blood Pressure Control in Aging Predicts Cerebral Atrophy Related to Small-Vessel White Matter Lesions

Cerebral small-vessel damage manifests as white matter hyperintensities and cerebral atrophy on brain MRI and is associated with aging, cognitive decline and dementia. We sought to examine the interrelationship of these imaging biomarkers and the influence of hypertension in older individuals. We used a multivariate spatial covariance neuroimaging technique to localize the effects of white matter lesion load on regional gray matter volume and assessed the role of blood pressure control, age and education on this relationship. Using a case-control design matching for age, gender, and educational attainment we selected 64 participants with normal blood pressure, controlled hypertension or uncontrolled hypertension from the Northern Manhattan Study cohort. We applied gray matter voxel-based morphometry with the scaled subprofile model to (1) identify regional covariance patterns of gray matter volume differences associated with white matter lesion load, (2) compare this relationship across blood pressure groups, and (3) relate it to cognitive performance. In this group of participants aged 60–86 years, we identified a pattern of reduced gray matter volume associated with white matter lesion load in bilateral temporal-parietal regions with relative preservation of volume in the basal forebrain, thalami and cingulate cortex. This pattern was expressed most in the uncontrolled hypertension group and least in the normotensives, but was also more evident in older and more educated individuals. Expression of this pattern was associated with worse performance in executive function and memory. In summary, white matter lesions from small-vessel disease are associated with a regional pattern of gray matter atrophy that is mitigated by blood pressure control, exacerbated by aging, and associated with cognitive performance.

Automatic tumor lesion detection and segmentation using modified winnow algorithm

Automated recognition of brain tumors in magnetic resonance images (MRI) is a difficult procedure due to the variability and complexity of the location, size, shape, and texture of these lesions. Due to intensity similarities between brain lesions and normal tissues, most approaches make use of multi-spectral MRI images. However, the time, cost, and data process restrictions for collecting multi-spectral MRI necessitate developing a lesion detection and segmentation approach that can detect lesions using a single anatomical MRI image. In this paper, we present a fully automatic system, which is able to detect the MRI images that include tumor and to segment the tumor area. Fully anisotropic complex Morlet transform, and dual tree complex wavelet transform are introduced for tumor textural characterization. Perhaps most importantly, we propose a novel feature selection technique that is based on regularized Winnow algorithm. An active contour model implemented with selective binary and Gaussian filtering regularized level set (SBGFRLS) is used for final segmentation step. The experimental results on both simulated and real brain MRI data prove the efficacy of our technique in successfully segmenting brain tumor tissues with high accuracy and low computational complexity.

Automatic Facial Expression Recognition Using Modified Wavelet-Based Salient Points and Gabor-Wavelet Filters

In this paper, we present an automated approach for recognizing seven facial expressions including the neutral expression. The approach is based upon efficient feature extraction, feature compression, and an artificial neural network (ANN) classification. In the proposed method, the basic components of face, eyes, eyebrow, and mouth, are first segmented from the whole face using modified Wavelet based salient points. Then, the features of the eye and the mouth are extracted using Gabor-wavelet filters. Afterwards, the dimension of the features is reduced using principal component analysis (PCA). Finally a multi layer perceptron neural network is used to classify the facial expressions. The simulated results show high recognition rate as well as the low computational complexity that makes the proposed algorithm remarkable for accurate and fast facial expression recognition.

Automatic tumor lesion detection and segmentation using histogram-based gravitational optimization algorithm

In this paper, an automated and customized brain tumor segmentation method is presented and validated against ground truth applying simulated T1-weighted magnetic resonance images in 25 subjects. A new intensity-based segmentation technique called histogram based gravitational optimization algorithm is developed to segment the brain image into discriminative sections (segments) with high accuracy. While the mathematical foundation of this algorithm is presented in details, the application of the proposed algorithm in the segmentation of single T1-weighted images (T1-w) modality of healthy and lesion MR images is also presented. The results show that the tumor lesion is segmented from the detected lesion slice with 89.6% accuracy.