Wavelet Features for Recognition of First Episode of Schizophrenia from MRI Brain Images

Machine learning methods are increasingly used in various fields of medicine, contributing to early diagnosis and better quality of care. These outputs are particularly desirable in case of neuropsychiatric disorders, such as schizophrenia, due to the inherent potential for creating a new gold standard in the diagnosis and differentiation of particular disorders. This paper presents a scheme for automated classification from magnetic resonance images based on multiresolution representation in the wavelet domain. Implementation of the proposed algorithm, utilizing support vector machines classifier, is introduced and tested on a dataset containing 104 patients with first episode schizophrenia and healthy volunteers. Optimal parameters of different phases of the algorithm are sought and the quality of classification is estimated by robust cross validation techniques. Values of accuracy, sensitivity and specificity over 71% are achieved

A state-of-the-art review of micron-scale spatially resolved residual stress analysis by FIB-DIC ring-core milling and other techniques

Quantification of residual stress gradients can provide great improvements in understanding the complex interactions between microstructure, mechanical state, mode(s) of failure and structural integrity. Highly focused local probe non-destructive techniques such as X-ray diffraction, electron diffraction or Raman spectroscopy have an established track record in determining spatial variations in the relative changes in residual stress with respect to a reference state for many structural materials. However, the interpretation of these measurements in terms of absolute stress values requires a strain-free sample often difficult to obtain due to the influence of chemistry, microstructure or processing route. With the increasing availability of focused ion beam instruments, a new approach has been developed which is known as the micro-scale ring-core focused ion beam-digital image correlation technique. This technique is becoming the principal tool for quantifying absolute in-plane residual stresses. It can be applied to a broad range of materials: crystalline and amorphous metallic alloys and ceramics, polymers, composites and biomaterials. The precise nano-scale positioning and well-defined gauge volume of this experimental technique make it eminently suitable for spatially resolved analysis, that is, residual stress profiling and mapping. Following a summary of micro-stress evaluation approaches, we focus our attention on focused ion beam-digital image correlation methods and assess the application of micro-scale ring-core methods for spatially resolved residual stress profiling. The sequential ring-core milling focused ion beam-digital image correlation method allows micro- to macro-scale mapping at the step of 10-1000 μm, while the parallel focused ion beam-digital image correlation approach exploits simultaneous milling operation to quantify stress profiles at the micron scale (1-10 μm). Cross-validation against X-ray diffraction results confirms that these approaches represent accurate, reliable and effective residual stress mapping methods