9,760 research outputs found
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
Analysis of the contour structural irregularity of skin lesions using wavelet decomposition
The boundary irregularity of skin lesions is of clinical significance for the early detection of
malignant melanomas and to distinguish them from other lesions such as benign moles. The
structural components of the contour are of particular importance. To extract the structure from
the contour, wavelet decomposition was used as these components tend to locate in the lower
frequency sub-bands. Lesion contours were modeled as signatures with scale normalization to
give position and frequency resolution invariance. Energy distributions among different wavelet
sub-bands were then analyzed to extract those with significant levels and differences to enable
maximum discrimination.
Based on the coefficients in the significant sub-bands, structural components from the original
contours were modeled, and a set of statistical and geometric irregularity descriptors researched
that were applied at each of the significant sub-bands. The effectiveness of the descriptors was
measured using the Hausdorff distance between sets of data from melanoma and mole contours.
The best descriptor outputs were input to a back projection neural network to construct a
combined classifier system. Experimental results showed that thirteen features from four
sub-bands produced the best discrimination between sets of melanomas and moles, and that a
small training set of nine melanomas and nine moles was optimum
A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data
A quantitative assessment of forest cover change in the Moulouya River watershed (Morocco) was carried out by means of an innovative approach from atmospherically corrected reflectance Landsat images corresponding to 1984 (Landsat 5 Thematic Mapper) and 2013 (Landsat 8 Operational Land Imager). An object-based image analysis (OBIA) was undertaken to classify segmented objects as forested or non-forested within the 2013 Landsat orthomosaic. A Random Forest classifier was applied to a set of training data based on a features vector composed of different types of object features such as vegetation indices, mean spectral values and pixel-based fractional cover derived from probabilistic spectral mixture analysis). The very high spatial resolution image data of Google Earth 2013 were employed to train/validate the Random Forest classifier, ranking the NDVI vegetation index and the corresponding pixel-based percentages of photosynthetic vegetation and bare soil as the most statistically significant object features to extract forested and non-forested areas. Regarding classification accuracy, an overall accuracy of 92.34% was achieved. The previously developed classification scheme was applied to the 1984 Landsat data to extract the forest cover change between 1984 and 2013, showing a slight net increase of 5.3% (ca. 8800 ha) in forested areas for the whole region
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