Content Based Image Retrieval of Brain MR Images across Different Classes

Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users’ feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved

Automatic Segmentation Framework for Primary Tumors From Brain MRIs Using Morphological Filtering Techniques

This paper describes a novel framework for automatic segmentation of primary tumors and its boundary from brain MRIs using morphological filtering techniques. This method uses T2 weighted and T1 FLAIR images. This approach is very simple, more accurate and less time consuming than existing methods. This method is tested by fifty patients of different tumor types, shapes, image intensities, sizes and produced better results. The results were validated with ground truth images by the radiologist. Segmentation of the tumor and boundary detection is important because it can be used for surgical planning, treatment planning, textural analysis, 3-Dimensional modeling and volumetric analysis

A semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the pre- operative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These seg- mented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming proc- ess and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of a medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radi- ologist.

Texture Description of low grade and high grade Glioma using Statistical features in Brain MRIs

Low grade and High grade Gliomas are tumors that originate in the glial cells. The main challenge in brain tumor diagnosis is whether a tumor is benign or malignant, primary or metastatic and low or high grade. Based on the patient's MRI, a radiologist could not differentiate whether it is a low grade Glioma or a high grade Glioma. Because both of these are almost visually similar, autopsy confirms the diagnosis of low grade with high-grade and infiltrative features. In this paper, textural description of Grade I and grade III Glioma are extracted using First order statistics and Gray Level Co-occurance Matrix Method (GLCM). Textural features are extracted from 16X16 sub image of the segmented Region of Interest(ROI) .In the proposed method, first order statistical features such as contrast, Intensity , Entropy, Kurtosis and spectral energy and GLCM features extracted were showed promising results. The ranges of these first order statistics and GLCM based features extracted are highly discriminant between grade I and Grade III. In this study which gives statistical textural information of grade I and grade III Glioma which is very useful for further classification and analysis and thus assisting Radiologist in greater extent.

Photoperiod-dependent plasticity of circadian pacemaker center in the brain of the Madeira cockroach Rhyparobia maderae

To various degrees, insects in nature adapt to and live with two fundamental environmental rhythms around them: (1) the daily rhythm of light and dark, and (2) the yearly seasonal rhythm of the changing photoperiod (length of light per day). It is hypothesized that two biological clocks evolved in organisms on earth which allow them to harmonize successfully with the two environmental rhythms: (1) the circadian clock, which orchestrates circadian rhythms in physiology and behavior, and (2) the photoperiodic clock, which allows for physiological adaptations to changes in photoperiod during the course of the year (insect photoperiodism). The circadian rhythm is endogenous and continues in constant conditions, while photoperiodism requires specific light inputs of a minimal duration. Output pathways from both clocks control neurosecretory cells which regulate growth and reproduction. This dissertation focuses on the question whether different photoperiods change the network and physiology of the circadian clock of an originally equatorial cockroach species. It is assumed that photoperiod-dependent plasticity of the cockroach circadian clock allows for adaptations in physiology and behavior without the need for a separate photoperiodic clock circuit. The Madeira cockroach Rhyparobia maderae is a well established circadian clock model system. Lesion and transplantation studies identified the accessory medulla (aMe), a small neuropil with about 250 neurons, as the cockroach circadian pacemaker. Among them, the pigment-dispersing factor immunoreactive (PDF-ir) neurons anterior to the aMe (aPDFMes) play a key role as inputs to and outputs of the circadian clock system. The aim of my doctoral thesis was to examine whether and how different photoperiods modify the circadian clock system. With immunocytochemical studies, three-dimensional (3D) reconstruction, standardization and Ca2+-imaging technique, my studies revealed that raising cockroaches in different photoperiods changed the neuronal network of the circadian clock (Wei and Stengl, 2011). In addition, different photoperiods affected the physiology of single, isolated circadian pacemaker neurons. This thesis provides new evidence for the involvement of the circadian clock in insect photoperiodism. The data suggest that the circadian pacemaker system of the Madeira cockroach has the plasticity and potential to allow for physiological adaptations to different photoperiods. Therefore, it may express also properties of a photoperiodic clock.

World Hunger, Malnutrition and Brain Development of Children

Hunger is still a major problem faced by people in the world especially in some areas in developing countries, and this condition is a cause of undernutrition. Insufficient nutrition during the early stages of life may adversely influence brain development. It was observed from my own research conducted in Bogor, Indonesia, that children with severe acute malnutrition (SAM, body mass index or BMI for age z score < -3) (N=54) had significantly (p<0.05) lower memory ability score (46.22±1.38) compared to normal children (BMI for age z score -2 ≤ z ≤ 1) (N=91) (51.56±1.24). Further, children with Moderate Acute Malnutrition (MAM, BMI for age z score -3 ≤ z <-2) tended to (p<0.1) have lower memory ability (50.08±1.58) than the normal children. On the other hand, overnutrition among children also might impair the brain function. The study revealed that children who are overweight (BMI for age z score 1 < z ≤ 2) (N=8) significantly (p<0.05) had lower memory ability score (46.13±4.50) compared to the normal children. This study also revealed that obese children (BMI for age z score > 2) (N=6) tended to (p<0.1) have lower memory ability score (50.33±5.64) than the normal children. It is therefore very important to maintain children at a normal BMI, not being undernourished (SAM and MAM categories) on one side and not being overnourished (overweight and obesity categories) on the other side in order to optimise their brain development. This could be achieved through providing children with an adequate and balanced nutrient supply via food.

Double burden malnutrition of preschool children and its association with brain development and milk consumption

Protecting the quality of children growth and development becomes a supreme qualification for the betterment of a nation. Double burden child malnutrition is emerging worldwide which might have a strong influence to the quality of child brain development and could not be paid-off on later life. Milk places a notable portion during the infancy and childhood. Thus, the deep insight on milk consumption pattern might explain the phenomenon of double burden child malnutrition correlated to the cognitive impairments. Objective: Current study is intended (1) to examine the current face of Indonesian double burden child malnutrition: a case study in Bogor, West Java, Indonesia, (2) to investigate the association of this phenomenon with child brain development, and (3) to examine the contribution of socioeconomic status and milk consumption on this phenomenon so that able to formulate some possible solutions to encounter this problem. Design: A cross-sectional study using a structured coded questionnaire was conducted among 387 children age 5-6 years old and their parents from 8 areas in Bogor, West-Java, Indonesia on November 2012 to December 2013, to record some socioeconomic status, anthropometric measurements, and history of breast feeding. Diet and probability of milk intake was assessed by two 24 h dietary recalls and food frequency questionnaire (FFQ). Usual daily milk intake was calculated using Multiple Source Method (MSM). Some brain development indicators (IQ, EQ, learning, and memory ability) using Projective Multi-phase Orientation method was also executed to learn the correlation between double burden child malnutrition and some brain development indicator. Results and conclusions: A small picture of child double burden malnutrition is shown in Bogor, West Java, Indonesia, where prevalence of Severe Acute Malnutrition (SAM) is 27.1%, Moderate Acute Malnutrition (MAM) is 24.9%, and overnutrition is 7.7%. This phenomenon proves to impair the child brain development. The malnourished children, both under- and over- nourished children have significantly (P-value<0.05) lower memory ability compared to the normal children (memory score, N; SAM = 45.2, 60; MAM = 48.5, 61; overweight = 48.4, 43; obesity = 47.9, 60; normal = 52.4, 163). The plausible reasons behind these evidences are the lack of nutrient intake during the sprout growth period on undernourished children or increasing adiposity on overnourished children might influence the growth of hippocampus area which responsible to the memory ability. Either undernutrition or overnutrition, the preventive action on this problem is preferable to avoid ongoing cognitive performance loss of the next generation. Some possible solutions for this phenomenon are promoting breast feeding initiation and exclusive breast feeding practices for infants, supporting the consumption of a normal portion of milk (250 to 500 ml per day) for children, and breaking the chain of poverty by socioeconomic improvement. And, the national food security becomes the fundamental point for the betterment of the next. In the global context, the causes of under- and over- nutrition have to be opposed through integrated and systemic approaches for a better quality of the next generation of human beings.

Segmentation of Brain Tissue from Magnetic Resonance Images

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.

How are Three-Deminsional Objects Represented in the Brain?

We discuss a variety of object recognition experiments in which human subjects were presented with realistically rendered images of computer-generated three-dimensional objects, with tight control over stimulus shape, surface properties, illumination, and viewpoint, as well as subjects' prior exposure to the stimulus objects. In all experiments recognition performance was: (1) consistently viewpoint dependent; (2) only partially aided by binocular stereo and other depth information, (3) specific to viewpoints that were familiar; (4) systematically disrupted by rotation in depth more than by deforming the two-dimensional images of the stimuli. These results are consistent with recently advanced computational theories of recognition based on view interpolation.

Normal brain

Images of normal brains

Utilidad de la electromiografía de esfínter anal en el diagnóstico diferencial de la atrofia multisistémica: una revisión de la literatura

La atrofia multisistémica (AMS) es una enfermedad degenerativa caracterizada por disautonomías y síntomas extrapiramidales. El diagnóstico diferencial con otros parkinsonismos es difícil, por lo cual se requiere una ayuda paraclínica para soportar el diagnóstico clínico. La degeneración del núcleo de Onuf, exclusiva en esta enfermedad, podría sugerir que la presencia de denervación en el esfínter anal podría ser tomada en cuenta como criterio diagnóstico de AMS. Se realizó una revisión sistemática con el fin de determinar la utilidad de la electromiografía de esfínter anal (EMG-EA) en el diagnóstico diferencial de AMS contra otros parkinsonismos. Se incluyeron 17 estudios que analizaron los resultados de EMG-EA en pacientes con AMS. De éstos, 11 de estudios fueron analíticos y compararon pacientes con AMS y otros parkinsonismos. Los 6 estudios restantes fueron descriptivos. La duración de los potenciales de unidad motora (PUM) es significativamente mayor en pacientes con AMS comparados con otros parkinsonismos, y utilizando un punto de corte > 13 ms muestra características operativas que hacen a este parámetro potencialmente útil. Solo un estudio encontró diferencias significativas en el porcentaje de PUM polifásicos, el cual tuvo una sensibilidad y especificidad clínicamente útil cuando el punto de corte es mayor a 60%. El resto de los estudios no reportan diferencias estadísticamente significativas entre parkinsonismos. La literatura disponible apunta a la potencial utilidad de la EMG-EA en el diagnóstico diferencial de la AMS de otros parkinsonismos; sin embargo es necesario conducir más estudios para solventar las limitaciones metodológicas existentes.

16/6-idiotype expressing antibodies induce brain inflammation and cognitive impairment in mice: the mosaic of central nervous system involvement in lupus

Background: The 16/6-idiotype (16/6-Id) of the human anti-DNA antibody was found to induce experimental lupus in naive mice, manifested by production of autoantibodies, leukopenia and elevated inflammatory markers, as well as kidney and brain involvement. We assessed behavior and brain pathology of naive mice injected intracerebra-ventricularly (ICV) with the 16/6-Id antibody. Methods: C3H female mice were injected ICV to the right hemisphere with the human 16/6-Id antibody or commercial human IgG antibodies (control). The mice were tested for depression by the forced swimming test (FST), locomotor and explorative activity by the staircase test, and cognitive functions were examined by the novel object recognition and Y-maze tests. Brain slices were stained for inflammatory processes. Results: 16/6-Id injected mice were cognitively impaired as shown by significant differences in the preference for a new object in the novel object recognition test compared to controls (P = 0.012). Similarly, the preference for spatial novelty in the Y-maze test was significantly higher in the control group compared to the 16/6-Id-injected mice (42% vs. 9%, respectively, P = 0.065). Depression-like behavior and locomotor activity were not significantly different between the16/6-Id-injected and the control mice. Immunohistochemistry analysis revealed an increase in astrocytes and microglial activation in the hippocampus and amygdala, in the 16/6-Id injected group compared to the control. Conclusions: Passive transfer of 16/6-Id antibodies directly into mice brain resulted in cognitive impairments and histological evidence for brain inflammation. These findings shed additional light on the diverse mosaic pathophysiology of neuropsychiatric lupus.