Grey-matter texture abnormalities and reduced hippocampal volume are distinguishing features of schizophrenia

Neurodevelopmental processes are widely believed to underlie schizophrenia. Analysis of brain texture from conventional magnetic resonance imaging (MRI) can detect disturbance in brain cytoarchitecture. We tested the hypothesis that patients with schizophrenia manifest quantitative differences in brain texture that, alongside discrete volumetric changes, may serve as an endophenotypic biomarker. Texture analysis (TA) of grey matter distribution and voxel-based morphometry (VBM) of regional brain volumes were applied to MRI scans of 27 patients with schizophrenia and 24 controls. Texture parameters (uniformity and entropy) were also used as covariates in VBM analyses to test for correspondence with regional brain volume. Linear discriminant analysis tested if texture and volumetric data predicted diagnostic group membership (schizophrenia or control). We found that uniformity and entropy of grey matter differed significantly between individuals with schizophrenia and controls at the fine spatial scale (filter width below 2 mm). Within the schizophrenia group, these texture parameters correlated with volumes of the left hippocampus, right amygdala and cerebellum. The best predictor of diagnostic group membership was the combination of fine texture heterogeneity and left hippocampal size. This study highlights the presence of distributed grey-matter abnormalities in schizophrenia, and their relation to focal structural abnormality of the hippocampus. The conjunction of these features has potential as a neuroimaging endophenotype of schizophrenia

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and in conjunction with decision-theoretic approaches used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue

Accurate detection of dysmorphic nuclei using dynamic programming and supervised classification

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows

Direct imaging of short-range order and its impact on deformation in Ti-6Al.

Chemical short-range order (SRO) within a nominally single-phase solid solution is known to affect the mechanical properties of alloys. While SRO has been indirectly related to deformation, direct observation of the SRO domain structure, and its effects on deformation mechanisms at the nanoscale, has remained elusive. Here, we report the direct observation of SRO in relation to deformation using energy-filtered imaging in a transmission electron microscope (TEM). The diffraction contrast is enhanced by reducing the inelastically scattered electrons, revealing subnanometer SRO-enhanced domains. The destruction of these domains by dislocation planar slip is observed after ex situ and in situ TEM mechanical testing. These results confirm the impact of SRO in Ti-Al alloys on the scale of angstroms. The direct confirmation of SRO in relationship to dislocation plasticity in metals can provide insight into how the mechanical behavior of concentrated solid solutions by the material's thermal history

Digital Dermatoscopy Method for Human Skin Roughness Analysis

In this study we  propose a digital dermatoscopy  method to measure the human skin roughness. By using this method we eliminate the use of silicon replica.  Digital  dermatoscopy  consists  of  handheld  digital  microscope,  image processing  and  information  extraction  of  skin  roughness  level.  To  reduce  the noise due to the variation of reflection factor on the skin we use  median filter. Hence, by Fourier transform the skin texture is imaged in terms of 2D frequencyspatial  distribution.  Skin  roughness  is  determined  from  its  entropy,  where  the roughness level is proportional to the entropy.  Three types of experiment have been performed by evaluating: (i) the skin replicas; (ii)  young and elderly skin; and (iii) seven volunteers treated by anti wrinkle cosmetic in three weeks period. We find that for the first and second experiment that our system did manage to quantify the roughness, while on the third experiment, six of seven volunteers, the roughness are succeeded to identify