65 research outputs found
A Method for the Quantification of Nanoparticle Dispersion in Nanocomposites Based on Fractal Dimension
Dispersion quantification provides critical insight and towards understanding and improving the influence of nanoparticle dispersion on the behaviour of the nanocomposite at macro and nanoscale level. This study was precipitated by the limitations of most methods for quantifying dispersion to sufficiently handle issues regarding scalability, complexity, consistency and versatility. A quantity (D 0 ) based on the variance of the fractal dimension was used to quantify dispersion successfully. The concept was validated using real microscopy images. The approach is simple and versatile to implement
Surface roughness detection of arteries via texture analysis of ultrasound images for early diagnosis of atherosclerosis
There is a strong research interest in identifying the surface roughness of the carotid arterial inner wall via texture analysis for early diagnosis of atherosclerosis. The purpose of this study is to assess the efficacy of texture analysis methods for identifying arterial roughness in the early stage of atherosclerosis. Ultrasound images of common carotid arteries of 15 normal mice fed a normal diet and 28 apoE−/− mice fed a high-fat diet were recorded by a high-frequency ultrasound system (Vevo 2100, frequency: 40 MHz). Six different texture feature sets were extracted based on the following methods: first-order statistics, fractal dimension texture analysis, spatial gray level dependence matrix, gray level difference statistics, the neighborhood gray tone difference matrix, and the statistical feature matrix. Statistical analysis indicates that 11 of 19 texture features can be used to distinguish between normal and abnormal groups (p<0.05). When the 11 optimal features were used as inputs to a support vector machine classifier, we achieved over 89% accuracy, 87% sensitivity and 93% specificity. The accuracy, sensitivity and specificity for the k-nearest neighbor classifier were 73%, 75% and 70%, respectively. The results show that it is feasible to identify arterial surface roughness based on texture features extracted from ultrasound images of the carotid arterial wall. This method is shown to be useful for early detection and diagnosis of atherosclerosis.Lili Niu, Ming Qian, Wei Yang, Long Meng, Yang Xiao, Kelvin K. L. Wong, Derek Abbott, Xin Liu, Hairong Zhen
Selective aggregation of PAMAM dendrimer nanocarriers and PAMAM/ZnPc nanodrugs on human atheromatous carotid tissues: a photodynamic therapy for atherosclerosis
AN EXACT LINE INTEGRAL REPRESENTATION OF THE PHYSICAL OPTICS FAR FIELD FROM PLANE PEC SCATTERERS ILLUMINATED BY HERTZIAN DIPOLES
A real time ECG signal processing application for arrhythmia detection on portable devices
Arrhythmia describes the disorders of normal heart rate, which,
depending on the case, can even be fatal for a patient with severe
history of heart disease. The purpose of this work is to develop an
application for heart signal visualization, processing and analysis in
Android portable devices e.g. Mobile phones, tablets, etc. The
application is able to retrieve the signal initially from a file and at
a later stage this signal is processed and analysed within the device so
that it can be classified according to the features of the arrhythmia.
In the processing and analysing stage, different algorithms are included
among them the Moving Average and Pan Tompkins algorithm as well as the
use of wavelets, in order to extract features and characteristics. At
the final stage, testing is performed by simulating our application in
realtime records, using the TCP network protocol for communicating the
mobile with a simulated signal source. The classification of ECG beat to
be processed is performed by neural networks
Comparison of different global and local automatic registration schemes: An application to retinal images
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