A novel method for power quality multiple disturbance decomposition based on Independent Component Analysis

In this paper, a novel method for power quality signal decomposition is proposed based on Independent Component Analysis (ICA). This method aims to decompose the power system signal (voltage or current) into components that can provide more specific information about the different disturbances which are occurring simultaneously during a multiple disturbance situation. The ICA is originally a multichannel technique. However, the method proposes its use to blindly separate out disturbances existing in a single measured signal (single channel). Therefore, a preprocessing step for the ICA is proposed using a filter bank. The proposed method was applied to synthetic data, simulated data, as well as actual power system signals, showing a very good performance. A comparison with the decomposition provided by the Discrete Wavelet Transform shows that the proposed method presented better decoupling for the analyzed data. (C) 2012 Elsevier Ltd. All rights reserved.

New structural brain imaging endophenotype in bipolar disorder

Neuroimaging studies suggest anterior-limbic structural brain abnormalities in patients with bipolar disorder (BD), but few studies have shown these abnormalities in unaffected but genetically liable family members. In this study, we report morphometric correlates of genetic risk for BD using voxel-based morphometry. In 35 BD type I (BD-I) patients, 20 unaffected first-degree relatives (UAR) of BD patients and 40 healthy control subjects underwent 3 T magnetic resonance scanner imaging. Preprocessing of images used DARTEL (diffeomorphic anatomical registration through exponentiated lie algebra) for voxel-based morphometry in SPM8 (Wellcome Department of Imaging Neuroscience, London, UK). The whole-brain analysis revealed that the gray matter (GM) volumes of the left anterior insula and right inferior frontal gyrus showed a significant main effect of diagnosis. Multiple comparison analysis showed that the BD-I patients and the UAR subjects had smaller left anterior insular GM volumes compared with the healthy subjects, the BD-I patients had smaller right inferior frontal gyrus compared with the healthy subjects. For white matter (WM) volumes, there was a significant main effect of diagnosis for medial frontal gyrus. The UAR subjects had smaller right medial frontal WM volumes compared with the healthy subjects. These findings suggest that morphometric brain abnormalities of the anterior-limbic neural substrate are associated with family history of BD, which may give insight into the pathophysiology of BD, and be a potential candidate as a morphological endophenotype of BD. Molecular Psychiatry (2012) 17, 412-420; doi: 10.1038/mp.2011.3; published online 15 February 2011

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Abstract Background Atherosclerosis causes millions of deaths, annually yielding billions in expenses round the world. Intravascular Optical Coherence Tomography (IVOCT) is a medical imaging modality, which displays high resolution images of coronary cross-section. Nonetheless, quantitative information can only be obtained with segmentation; consequently, more adequate diagnostics, therapies and interventions can be provided. Since it is a relatively new modality, many different segmentation methods, available in the literature for other modalities, could be successfully applied to IVOCT images, improving accuracies and uses. Method An automatic lumen segmentation approach, based on Wavelet Transform and Mathematical Morphology, is presented. The methodology is divided into three main parts. First, the preprocessing stage attenuates and enhances undesirable and important information, respectively. Second, in the feature extraction block, wavelet is associated with an adapted version of Otsu threshold; hence, tissue information is discriminated and binarized. Finally, binary morphological reconstruction improves the binary information and constructs the binary lumen object. Results The evaluation was carried out by segmenting 290 challenging images from human and pig coronaries, and rabbit iliac arteries; the outcomes were compared with the gold standards made by experts. The resultant accuracy was obtained: True Positive (%) = 99.29 ± 2.96, False Positive (%) = 3.69 ± 2.88, False Negative (%) = 0.71 ± 2.96, Max False Positive Distance (mm) = 0.1 ± 0.07, Max False Negative Distance (mm) = 0.06 ± 0.1. Conclusions In conclusion, by segmenting a number of IVOCT images with various features, the proposed technique showed to be robust and more accurate than published studies; in addition, the method is completely automatic, providing a new tool for IVOCT segmentation.