Michelle Yan

Sex-related differences in behavior are extensive, but their neuroanatomic substrate is unclear. Indirect perfusion data have suggested a higher percentage of gray matter (GM) in left hemisphere cortex and in women, but differences in volumes of the major cranial compartments have not been examined for the entire brain in association with cognitive performance. We used volumetric segmentation of dual echo (proton density and T2-weighted) magnetic resonance imaging (MRI) scans in healthy volunteers (40 men, 40 women) age 18-45. Supertentorial volume was segmented into GM, white matter (WM), and CSF. We confirmed that women have a higher percentage of GM, whereas men have a higher percentage of WM and of CSF. These differences sustained a correction for total intracranial volume. In men the slope of the relation between cranial volume and GM paralleled that for WM, whereas in women the increase in WM as a function of cranial volume was at a lower rate. In men the percentage of GM was higher in the left hemisphere, the percentage of WM was symmetric, and the percentage of CSF was higher in the right. Women showed no asymmetries. Both GM and WM volumes correlated moderately with global, verbal, and spatial performance across groups. However, the regression of cognitive performance and WM volume was significantly steeper in women. Because GM consists of the somatodendritic tissue of neurons whereas WM comprises myelinated connecting axons, the higher percentage of GM makes more tissue available for computation relative to transfer across distant regions. This could compensate for smaller intracranial space in women. Sex difference in the percentage and asymmetry of the principal cranial tissue volumes may contribute to differences in cognitive functioning.

In this work, we developed, implemented, and validated an image-processing system for qualitative and quantitative volumetric analysis of brain images. This system allows the visualization and quantitation of global and regional brain volumes. Global volumes were obtained via an automated adaptive Bayesian segmentation technique that labels the brain into white matter, gray matter, and cerebrospinal fluid. Absolute volumetric errors for these compartments ranged between 1 and 3% as indicated by phantom studies. Quantitation of regional brain volumes was performed through normalization and tessellation of segmented brain images into the Talairach space with a 3D elastic warping model. Retest reliability of regional volumes measured in Talairach space indicated errors of < 1.5% for the frontal, parietal, temporal, and occipital brain regions. Additional regional analysis was performed with an automated hybrid method combining a region-of-interest approach and voxel-based analysis, named Regional Analysis of Volumes Examined in Normalized Space (RAVENS). RAVENS analysis for several subcortical structures showed good agreement with operator-defined volumes. This system has sufficient accuracy for longitudinal imaging data and is currently being used in the analysis of neuroimaging data of the Baltimore Longitudinal Study of Aging.

BACKGROUND: Cortical gray matter volume reductions and cerebrospinal fluid (CSF) volume increases are robust correlates of schizophrenia, but their sources have not been established conclusively. METHODS: Structured diagnostic interviews and magnetic resonance imaging scans of the brain were obtained on 75 psychotic probands (63 with schizophrenia and 12 with schizoaffective disorder), ascertained so as to be representative of all such probands in a Helsinki, Finland, birth cohort; 60 of their nonpsychotic full siblings; and 56 demographically similar control subjects without a personal or family history of treated psychiatric morbidity. RESULTS: Patients with schizophrenia and their siblings exhibited significant reductions in cortical gray matter volume and significant increases in sulcal CSF volume compared with controls. The patients, but not their siblings, also exhibited significant reductions in white matter volume and significant increases in ventricular CSF volume. Regional effects were most robust when component volumes were expressed as percentages of overall regional volumes; in this case, for patient and sibling groups, gray matter volume reductions and sulcal CSF volume increases were significantly more pronounced in the frontal and temporal lobes than in the remainder of the brain. None of the group differences varied significantly by sex or hemisphere. CONCLUSIONS: Structural alterations of the cerebral cortex, particularly in the frontal and temporal lobes, are present in patients with schizophrenia and in some of their siblings without schizophrenia; such changes are thus likely to reflect genetic (or shared environmental) effects. Ventricular enlargement is unique to the clinical phenotype and is thus likely to be affected primarily by nonshared causative factors.

The prevalence of diabetes is expected to increase dramatically in coming years; already today it accounts for a major proportion of the health care budget in many countries. Diabetic Retinopathy (DR), a micro vascular complication very often seen in diabetes patients, is the most common cause of visual loss in working age population of developed countries today. Since the possibility of slowing or even stopping the progress of this disease depends on the early detection of DR, an automatic analysis of fundus images would be of great help to the ophthalmologist due to the small size of the symptoms and the large number of patients. An important symptom for DR are abnormally wide veins leading to an unusually low ratio of the average diameter of arteries to veins (AVR). There are also other diseases like high blood pressure or diseases of the pancreas with one symptom being an abnormal AVR value. To determine it, a classification of vessels as arteries or veins is indispensable. As to our knowledge despite the importance there have only been two approaches to vessel classification yet. Therefore we propose an improved method. We compare two feature extraction methods and two classification methods based on support vector machines and neural networks. Given a hand-segmentation of vessels our approach achieves 95.32% correctly classified vessel pixels. This value decreases by 10% on average, if the result of a segmentation algorithm is used as basis for the classification.

A major issue in telepathology is the extremely large and growing size of digitized "virtual" slides, which can require several gigabytes of storage and cause significant delays in data transmission for remote image interpretation and interactive visualization by pathologists. Compression can reduce this massive amount of virtual slide data, but reversible (lossless) methods limit data reduction to less than 50%, while lossy compression can degrade image quality and diagnostic accuracy. "Visually lossless" compression offers the potential for using higher compression levels without noticeable artifacts, but requires a rate-control strategy that adapts to image content and loss visibility. We investigated the utility of a visual discrimination model (VDM) and other distortion metrics for predicting JPEG 2000 bit rates corresponding to visually lossless compression of virtual slides for breast biopsy specimens. Threshold bit rates were determined experimentally with human observers for a variety of tissue regions cropped from virtual slides. For test images compressed to their visually lossless thresholds, just-noticeable difference (JND) metrics computed by the VDM were nearly constant at the 95th percentile level or higher, and were significantly less variable than peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics. Our results suggest that VDM metrics could be used to guide the compression of virtual slides to achieve visually lossless compression while providing 5-12 times the data reduction of reversible methods.