COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier.

This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images using a lean transfer learning-based model. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the uppermost and lowermost slices were removed, preserving sixty percent of each patient's slices. Secondly, all slices underwent manual cropping to emphasize the lung areas. Subsequently, resized CT scans (224 × 224) were input into an Xception transfer learning model with a modified output. Both Xception's architecture and pretrained weights were leveraged in the method. A big and rigorously annotated database of CT images was used to verify the method. The number of patients/subjects in the dataset is more than 5000, and the number and shape of the slices in each CT scan varies greatly. Verification was made both on the validation partition and on the test partition of unseen images. Results on the COV19-CT database showcased not only improvement from our previous solution and the baseline but also comparable performance to the highest-achieving methods on the same dataset. Further validation studies could explore the scalability and adaptability of the developed methodologies across diverse healthcare settings and patient populations. Additionally, investigating the integration of advanced image processing techniques, such as automated region of interest detection and segmentation algorithms, could enhance the efficiency and accuracy of COVID-19 diagnosis.

Dual-infinite coordination polymer-engineered nanomedicines for dual-ion interference-mediated oxidative stress-dependent tumor suppression.

Recently, nanomedicine design has shifted from simple nanocarriers to nanodrugs with intrinsic antineoplastic activities for therapeutic performance optimization. In this regard, degradable nanomedicines containing functional inorganic ions have blazed a highly efficient and relatively safe ion interference paradigm for cancer theranostics. Herein, given the potential superiorities of infinite coordination polymers (ICPs) in degradation peculiarity and functional integration, a state-of-the-art dual-ICP-engineered nanomedicine is elaborately fabricated via integrating ferrocene (Fc) ICPs and calcium-tannic acid (Ca-TA) ICPs. Thereinto, Fc ICPs, and Ca-TA ICPs respectively serve as suppliers of ferrous iron ions (Fe2+) and calcium ions (Ca2+). After the acid-responsive degradation of ICPs, released TA from Ca-TA ICPs facilitated the conversion of released ferric iron (Fe3+) from Fc ICPs into highly active Fe2+. Owing to the dual-path oxidative stress and neighboring effect mediated by Fe2+ and Ca2+, such a dual-ICP-engineered nanomedicine effectively induces dual-ion interference against triple-negative breast cancer (TNBC). Therefore, this work provides a novel antineoplastic attempt to establish ICP-engineered nanomedicines and implement ion interference-mediated synergistic therapy.

An interactive time series image analysis software for dendritic spines.

Live fluorescence imaging has demonstrated the dynamic nature of dendritic spines, with changes in shape occurring both during development and in response to activity. The structure of a dendritic spine correlates with its functional efficacy. Learning and memory studies have shown that a great deal of the information stored by a neuron is contained in the synapses. High precision tracking of synaptic structures can give hints about the dynamic nature of memory and help us understand how memories evolve both in biological and artificial neural networks. Experiments that aim to investigate the dynamics behind the structural changes of dendritic spines require the collection and analysis of large time-series datasets. In this paper, we present an open-source software called SpineS for automatic longitudinal structural analysis of dendritic spines with additional features for manual intervention to ensure optimal analysis. We have tested the algorithm on in-vitro, in-vivo, and simulated datasets to demonstrate its performance in a wide range of possible experimental scenarios.

Channel Attention Networks for Robust MR Fingerprint Matching.

OBJECTIVE: Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of multiple tissue parameters such as T1 and T2 relaxation times. The working principle of MRF relies on varying acquisition parameters pseudo-randomly, so that each tissue generates its unique signal evolution during scanning. Even though MRF provides faster scanning, it has disadvantages such as erroneous and slow generation of the corresponding parametric maps, which needs to be improved. Moreover, there is a need for explainable architectures for understanding the guiding signals to generate accurate parametric maps. METHODS: In this paper, we addressed both of these shortcomings by proposing a novel neural network architecture (CONV-ICA) consisting of a channel-wise attention module and a fully convolutional network. Another contribution of this study is a new channel selection method: attention-based channel selection. Furthermore, the effect of patch size and temporal frames of MRF signal on channel reduction are analyzed by employing a channel-wise attention. RESULTS: The proposed approach, evaluated over 3 simulated MRF signals, reduces error in the reconstruction of tissue parameters by 8.88% for T1 and 75.44% for T2 with respect to state-of-the-art methods. CONCLUSION: It is demonstrated that channel attention mechanism helps to focus on informative channels and fully convolutional network extracts spatial information achieve the best reconstruction performance. SIGNIFICANCE: As a consequence of improvement in fast and accurate manner, presented work can contribute to make MRF appropriate for clinical use.

BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows.

Image analysis is key to extracting quantitative information from scientific microscopy images, but the methods involved are now often so refined that they can no longer be unambiguously described by written protocols. We introduce BIAFLOWS, an open-source web tool enabling to reproducibly deploy and benchmark bioimage analysis workflows coming from any software ecosystem. A curated instance of BIAFLOWS populated with 34 image analysis workflows and 15 microscopy image datasets recapitulating common bioimage analysis problems is available online. The workflows can be launched and assessed remotely by comparing their performance visually and according to standard benchmark metrics. We illustrated these features by comparing seven nuclei segmentation workflows, including deep-learning methods. BIAFLOWS enables to benchmark and share bioimage analysis workflows, hence safeguarding research results and promoting high-quality standards in image analysis. The platform is thoroughly documented and ready to gather annotated microscopy datasets and workflows contributed by the bioimaging community.

SSNOMBACTER: A collection of scattering-type scanning near-field optical microscopy and atomic force microscopy images of bacterial cells.

BACKGROUND: In recent years, a variety of imaging techniques operating at nanoscale resolution have been reported. These techniques have the potential to enrich our understanding of bacterial species relevant to human health, such as antibiotic-resistant pathogens. However, owing to the novelty of these techniques, their use is still confined to addressing very particular applications, and their availability is limited owing to associated costs and required expertise. Among these, scattering-type scanning near field optical microscopy (s-SNOM) has been demonstrated as a powerful tool for exploring important optical properties at nanoscale resolution, depending only on the size of a sharp tip. Despite its huge potential to resolve aspects that cannot be tackled otherwise, the penetration of s-SNOM into the life sciences is still proceeding at a slow pace for the aforementioned reasons. RESULTS: In this work we introduce SSNOMBACTER, a set of s-SNOM images collected on 15 bacterial species. These come accompanied by registered Atomic Force Microscopy images, which are useful for placing nanoscale optical information in a relevant topographic context. CONCLUSIONS: The proposed dataset aims to augment the popularity of s-SNOM and for accelerating its penetration in life sciences. Furthermore, we consider this dataset to be useful for the development and benchmarking of image analysis tools dedicated to s-SNOM imaging, which are scarce, despite the high need. In this latter context we discuss a series of image processing and analysis applications where SSNOMBACTER could be of help.

Modified Visual Magnetic Resonance Rating Scale for Evaluation of Patients with Forgetfulness.

BACKGROUND: As cognitive impairment increases with age, sulcal atrophy (SA) and the enlargement of the ventricles also increase. Considering the measurements on the previously proposed visual scales, a new scale is proposed in this study that allows us to evaluate the atrophy, white matter hyperintensities (WMHs), basal ganglia infarct (BGI), and infratentorial infarct (ITI) together. Our aim of this study is to propose a practical and standardized MRI for the clinicians to be used in daily practice. METHODS: A total of 97 patients older than 60 years and diagnosed with depression or Alzheimer's disease (AD) are included. Cranial MRI, Mini Mental State Examination (MMSE), detailed neuropsychometric tests, and depression scales are applied to all patients. The SA, ventricular atrophy (VA), medial temporal lobe atrophy (MTA), periventricular WMH (PWMH), subcortical WMH (SCWMH), BGI, and ITI are scored according to the scale. The total score is also recorded. RESULTS: The average age of the patients was 74.53, and the mean MMSE score was 22.7 in the degenerative group and 27.8 in the non-degenerative group. Among the patients, 50 were diagnosed with AD. All parameters significantly increased with age. In the degenerative group, SA, VA, MTA, PWMH, SCWMH, and total scores were found to be significantly higher. Sensitivities of VA, PWMH, SCWMH, and total scores, as well as both sensitivity and specificities of MTA score, were observed to be high. When they were combined, sensitivities and specificities were found to be high. CONCLUSION: The scale is observed to be predictive in discriminating degenerative and non-degenerative processes. This discrimination is important, particularly in depressive patients complaining of forgetfulness.

CONTEXTE: Dans la mesure où les manifestations de déficience cognitive ont tendance à augmenter avec le vieillissement, on constate aussi une augmentation de l'atrophie des sillons du cortex cérébral et de l'élargissement des ventricules cérébraux. En tenant compte des mesures propres à des échelles visuelles utilisées antérieurement, cette étude entend proposer une nouvelle échelle nous permettant d'évaluer en même temps des cas d'atrophie ainsi que la présence d'hyperdensités de la substance blanche, d'anomalies des ganglions de la base et d'infarctus affectant l'étage sus-tentoriel (infratentorial infarcts). L'objectif de cette étude est donc de proposer un examen d'IRM pratique et standardisé pouvant être utilisé quotidiennement par les cliniciens. MÉTHODES: Nous avons inclus dans cette étude 97 patients âgés de plus de 60 ans qui étaient soit atteints de dépression, soit de la maladie d'Alzheimer. Tous les patients recrutés ont été soumis à des examens d'IRM crâniens, au test de Folstein (ou MMSE), à un ensemble de tests neuro-psychométriques approfondis et à des échelles diagnostiques permettant d'évaluer la dépression. L'incidence de l'atrophie des sillons du cortex cérébral, de la région ventriculaire, du lobe temporal médian, des régions péri-ventriculaire et sous-corticale et de la substance blanche qu'elles contiennent, d'anomalies affectant les ganglions de base et d'infarctus à l'étage sus-tentoriel a été ainsi mesurée selon notre échelle. Le score total obtenu a aussi été enregistré. RÉSULTATS: L'âge moyen des patients était de 74,53 ans. Leur score moyen au test de Folstein était de 22,7 dans le cas du groupe de patients atteints d'une maladie dégénérative et de 27,8 dans le cas du groupe de patients n'étant pas atteints par ce type de maladie. Fait à noter, cinquante patients avaient reçu un diagnostic de maladie d'Alzheimer. Tous les paramètres évalués ont augmenté de façon notable avec l'âge. Ainsi, tant les scores obtenus dans le cas de l'atrophie des sillons du cortex cérébral, de celle affectant le lobe temporal médian, la région ventriculaire, la région péri-ventriculaire, la région sous-corticale que les scores totaux se sont révélés nettement plus élevés au sein du groupe de patients atteints d'une maladie dégénérative. La sensibilité des scores totaux et des scores évaluant l'atrophie des régions vasculaire, péri-vasculaire et sous-corticale, de même que la sensibilité et la spécificité des scores évaluant l'atrophie du lobe temporal médian, se sont révélées élevées. Lorsque combinées, la sensibilité et la spécificité sont apparues élevées. CONCLUSIONS: Notre échelle possède un caractère prédictif en ce qu'elle permet d'établir une distinction entre les processus dégénératifs et les processus non-dégénératifs. Cette capacité est particulièrement importante dans le cas de patients dépressifs qui se plaignent de perte de mémoire.

Tracking-assisted Detection of Dendritic Spines in Time-Lapse Microscopic Images.

Detecting morphological changes of dendritic spines in time-lapse microscopy images and correlating them with functional properties such as memory and learning, are fundamental and challenging problems in neurobiology research. In this paper, we propose an algorithm for dendritic spine detection in time series. The proposed approach initially performs spine detection at each time point and improves the accuracy by exploiting the information obtained from tracking of individual spines over time. To detect dendritic spines in a time point image we employ an SVM classifier trained by pre-labeled SIFT feature descriptors in combination with a dot enhancement filter. Second, to track the growth or loss of spines, we apply a SIFT-based rigid registration method for the alignment of time-series images. This step takes into account both the structure and the movement of objects, combined with a robust dynamic scheme to link information about spines that disappear and reappear over time. Next, we improve spine detection by employing a probabilistic dynamic programming approach to search for an optimum solution to accurately detect missed spines. Finally, we determine the spine location more precisely by performing a watershed-geodesic active contour model. We quantitatively assess the performance of the proposed spine detection algorithm based on annotations performed by biologists and compare its performance with the results obtained by the noncommercial software NeuronIQ. Experiments show that our approach can accurately detect and quantify spines in 2-photon microscopy time-lapse data and is able to accurately identify spine elimination and formation.

Nonparametric Joint Shape and Feature Priors for Image Segmentation.

In many image segmentation problems involving limited and low-quality data, employing statistical prior information about the shapes of the objects to be segmented can significantly improve the segmentation result. However, defining probability densities in the space of shapes is an open and challenging problem, especially if the object to be segmented comes from a shape density involving multiple modes (classes). Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. In these methods, the evolving curve may converge to a shape from a wrong mode of the posterior density when the observed intensities provide very little information about the object boundaries. In such scenarios, employing both shape- and class-dependent discriminative feature priors can aid the segmentation process. Such features may involve, e.g., intensity-based, textural, or geometric information about the objects to be segmented. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors constructed by Parzen density estimation. We incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation. The resulting optimization problem is solved using active contours. We present experimental results on a variety of synthetic and real data sets from several fields involving multimodal shape densities. Experimental results demonstrate the potential of the proposed method.

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.

In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

Dendritic spine classification using shape and appearance features based on two-photon microscopy.

BACKGROUND: Neuronal morphology and function are highly coupled. In particular, dendritic spine morphology is strongly governed by the incoming neuronal activity. The first step towards understanding the structure-function relationships is to classify spine shapes into the main spine types suggested in the literature. Due to the lack of reliable automated analysis tools, classification is mostly performed manually, which is a time-intensive task and prone to subjectivity. NEW METHOD: We propose an automated method to classify dendritic spines using shape and appearance features based on challenging two-photon laser scanning microscopy (2PLSM) data. Disjunctive Normal Shape Models (DNSM) is a recently proposed parametric shape representation. We perform segmentation of spine images by applying DNSM and use the resulting representation as shape features. Furthermore, we use Histogram of oriented gradients (HOG) to extract appearance features. In this context, we propose a kernel density estimation (KDE) based framework for dendritic spine classification, which uses these shape and appearance features. RESULTS: Our shape and appearance features based approach combined with Neural Network (NN) correctly classifies 87.06% of spines on a dataset of 456 spines. COMPARISON WITH EXISTING METHODS: Our proposed method outperforms standard morphological feature based approaches. Our KDE based framework also enables neuroscientists to analyze the separability of spine shape classes in the likelihood ratio space, which leads to further insights about nature of the spine shape analysis problem. CONCLUSIONS: Results validate that performance of our proposed approach is comparable to a human expert. It also enable neuroscientists to study shape statistics in the likelihood ratio space.

Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge.

Structural MRI is widely used for investigating brain atrophy in many neurodegenerative disorders, with several research groups developing and publishing techniques to provide quantitative assessments of this longitudinal change. Often techniques are compared through computation of required sample size estimates for future clinical trials. However interpretation of such comparisons is rendered complex because, despite using the same publicly available cohorts, the various techniques have been assessed with different data exclusions and different statistical analysis models. We created the MIRIAD atrophy challenge in order to test various capabilities of atrophy measurement techniques. The data consisted of 69 subjects (46 Alzheimer's disease, 23 control) who were scanned multiple (up to twelve) times at nine visits over a follow-up period of one to two years, resulting in 708 total image sets. Nine participating groups from 6 countries completed the challenge by providing volumetric measurements of key structures (whole brain, lateral ventricle, left and right hippocampi) for each dataset and atrophy measurements of these structures for each time point pair (both forward and backward) of a given subject. From these results, we formally compared techniques using exactly the same dataset. First, we assessed the repeatability of each technique using rates obtained from short intervals where no measurable atrophy is expected. For those measures that provided direct measures of atrophy between pairs of images, we also assessed symmetry and transitivity. Then, we performed a statistical analysis in a consistent manner using linear mixed effect models. The models, one for repeated measures of volume made at multiple time-points and a second for repeated "direct" measures of change in brain volume, appropriately allowed for the correlation between measures made on the same subject and were shown to fit the data well. From these models, we obtained estimates of the distribution of atrophy rates in the Alzheimer's disease (AD) and control groups and of required sample sizes to detect a 25% treatment effect, in relation to healthy ageing, with 95% significance and 80% power over follow-up periods of 6, 12, and 24months. Uncertainty in these estimates, and head-to-head comparisons between techniques, were carried out using the bootstrap. The lateral ventricles provided the most stable measurements, followed by the brain. The hippocampi had much more variability across participants, likely because of differences in segmentation protocol and less distinct boundaries. Most methods showed no indication of bias based on the short-term interval results, and direct measures provided good consistency in terms of symmetry and transitivity. The resulting annualized rates of change derived from the model ranged from, for whole brain: -1.4% to -2.2% (AD) and -0.35% to -0.67% (control), for ventricles: 4.6% to 10.2% (AD) and 1.2% to 3.4% (control), and for hippocampi: -1.5% to -7.0% (AD) and -0.4% to -1.4% (control). There were large and statistically significant differences in the sample size requirements between many of the techniques. The lowest sample sizes for each of these structures, for a trial with a 12month follow-up period, were 242 (95% CI: 154 to 422) for whole brain, 168 (95% CI: 112 to 282) for ventricles, 190 (95% CI: 146 to 268) for left hippocampi, and 158 (95% CI: 116 to 228) for right hippocampi. This analysis represents one of the most extensive statistical comparisons of a large number of different atrophy measurement techniques from around the globe. The challenge data will remain online and publicly available so that other groups can assess their methods.

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study.

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.

Single syllable tongue motion analysis using tagged cine MRI.

The complicated muscle activity of the human tongue and the resultant surface shapes can give us important clues about speech motor control and pathological tongue motion. This study uses tagged magnetic resonance imaging to provide a 2D surface deformation analysis of the tongue, as well as a 4D compression-expansion analysis, during utterances of four different syllables (/ba/, /ta/, /sha/ and /ga/). All speech tasks were performed several times to confirm the repeatability of the motion analysis. The results showed that the tongue has unique motion patterns for utterances of different syllables, and these differences, which may not be observed by a simple surface analysis, can be examined thoroughly by a 4D motion model-based analysis of the tongue muscles.

Local and global volume changes of subcortical brain structures from longitudinally varying neuroimaging data for dementia identification.

Quantification of structural changes in the human brain is important to elicit resemblances and differences between pathological and normal aging. Identification of dementia, associated with loss of cognitive ability beyond normal aging, and especially converters--the subgroup of individuals at risk for developing dementia--has recently gained importance. For this purpose atrophy markers have been explored and their effectiveness has been evaluated both cross-sectionally and longitudinally. However, more research is needed to understand the dynamics of atrophy markers at different disease stages, which requires temporal analysis of local along with global changes. Unfortunately, most of the longitudinal neuroimaging data available in the clinical settings is acquired at largely varying time intervals. In the light of the above, this study presents a novel methodology to process longitudinal neuroimaging data acquired incompletely and at different time intervals, and explores complementary nature of local and global brain volume changes in identifying dementia. Results on the OASIS database demonstrate discriminative power of global atrophy in hippocampus (as early as two years after the first visit) for identifying demented cases, and local volume shrinkage of thalamus proper (as early as three years after the first visit) for differentiating converters.

Coupled nonparametric shape and moment-based intershape pose priors for multiple basal ganglia structure segmentation.

This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and intershape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance images. We present a set of 2-D and 3-D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy.

Local structure-based region-of-interest retrieval in brain MR images.

The aging population and the growing amount of medical data have increased the need for automated tools in the neurology departments. Although the researchers have been developing computerized methods to help the medical expert, these efforts have primarily emphasized to improve the effectiveness in single patient data, such as computing a brain lesion size. However, patient-to-patient comparison that should help improve diagnosis and therapy has not received much attention. To this effect, this paper introduces a fast and robust region-of-interest retrieval method for brain MR images. We make the following various contributions to the domains of brain MR image analysis, and search and retrieval system: 1) we show the potential and robustness of local structure information in the search and retrieval of brain MR images; 2) we provide analysis of two complementary features, local binary patterns (LBPs) and Kanade-Lucas-Tomasi feature points, and their comparison with a baseline method; 3) we show that incorporating spatial context in the features substantially improves accuracy; and 4) we automatically extract dominant LBPs and demonstrate their effectiveness relative to the conventional LBP approach. Comprehensive experiments on real and simulated datasets revealed that dominant LBPs with spatial context is robust to geometric deformations and intensity variations, and have high accuracy and speed even in pathological cases. The proposed method can not only aid the medical expert in disease diagnosis, or be used in scout (localizer) scans for optimization of acquisition parameters, but also supports low-power handheld devices.

Robustness of local binary patterns in brain MR image analysis.

The aging population in developed countries has shifted considerable research attention to diseases related to age. Because age is one of the highest risk factors for neurodegenerative diseases, the need for automated brain image analysis has significantly increased. Magnetic Resonance Imaging (MRI) is a commonly used modality to image brain. MRI provides high tissue contrast; hence, the existing brain image analysis methods have often preferred the intensity information to others, such as texture. Recently, an easy-to-compute texture descriptor, Local Binary Pattern (LBP), has shown promise in various applications outside the medical field. In this paper, after extensive experiments, we show that rotation-invariant LBP is invariant to some common MRI artifacts that makes it possible to use it in various high-level brain MR image analysis applications.