Optimization studies and fabrication of an Fe-Mm(Dy)-B permanent magnet using powder metallurgy techniques

Call number: LD2668 .T4 CHE 1987 C57Master of ScienceChemical Engineerin

AM-FM texture image analysis of the intima and media layers of the carotid artery

The purpose of this paper is to propose the use of amplitude modulation-frequency modulation (AM-FM) features for describing atherosclerotic plaque features that are associated with clinical factors such as intima media thickness and a patient's age. AM-FM analysis reveals the instantaneous amplitude (IA) of the media layer decreases with age. This decrease in IA maybe attributed to the reduction in calcified, stable plaque components and an increase in stroke risk with age. On the other hand, an increase in the median instantaneous frequency (IF) of the media layer suggests the fragmentation of solid, large plaque components, which also lead to an increase in the risk of stroke. The findings suggest that AM-FM features can be used to assess the risk of stroke over a wide range of patient populations. Future work will incorporate a new texture image retrieval system that uses AM-FM features to retrieve intima and intima media layer images that could be associated with the same level of the risk of stroke

De-speckle filtering in ultrasound imaging of the carotid artery

The main objective of this paper is to evaluate the classification performance of de-speckle filtering on ultrasound imaging of the carotid atherosclerotic plaque. The following procedure was investigated on 230 images (recorded from 115 symptomatic, and 115 asymptomatic subjects): (i) six different de-speckle filters were used based on first order and higher order local statistics, anisotropic diffusion, and geometric properties; (ii) nine different texture feature sets were extracted, and (iii) the k-nearest neighbor classifier was used to classify a plaque as symptomatic or asymptomatic. The de-speckle filters based on higher order statistics, anisotropic speckle diffusion, and geometric properties gave a slightly higher percentage of correct classifications score than the original images

Assessment of stroke by analysing carotid plaque morphology

Stroke is the third leading cause of death in the Western world and a major cause of disability in adults. The objective of this work was to investigate morphological feature extraction techniques and the use of automatic classifiers; in order to develop a computer aided system that will facilitate the automated characterization of carotid plaques for the identification of individuals with asymptomatic carotid stenosis at risk of stroke. Through this chapter we summarize the recent advances in ultrasonic plaque characterization and evaluate the efficacy of computer aided diagnosis based on neural and statistical classifiers using as input morphological features. Several classifiers like the K-Nearest Neighbour(KNN) the Probabilistic Neural Network(PNN) and the Support Vector Machine(SVM) were evaluated resulting to a diagnostic accuracy up to 73.7%

An integrated system for assessing stroke risk

The objective of this article is to present an integrated system for the assessment of risk of stroke based on two modules: 1) clinical risk factors and noninvasive investigations, and 2) carotid plaque texture analysis. The system supports the data collected from the Asymptomatic Carotid Stenosis and Risk of Stroke (ACSRS) natural history study that includes following data: 1) clinical, biochemical, and electrocardiographic risk factors associated with cardiovascular deaths, and 2) whether any independent risk factors can be used to identify a high-risk group for cardiovascular death. Furthermore, the carotid plaque texture analysis module supports the normalization, despeckling, segmentation, texture feature extraction, and classification of ultrasound plaque images

Prediction of the time period of stroke based on ultrasound image analysis of initially asymptomatic carotid plaques

Non-invasive ultrasound imaging of carotid plaques can provide information on the characteristics of the arterial wall including the size, morphology and texture of the atherosclerotic plaques. Several studies were carried out that demonstrated the usefulness of these feature sets for differentiating between asymptomatic and symptomatic plaques and their corresponding cerebrovascular risk stratification. The aim of this study was to develop predictive modelling for estimating the time period of a stroke event by determining the risk for short term (less or equal to three years) or long term (more than three years) events. Data from 108 patients that had a stroke event have been used. The information collected included clinical and ultrasound imaging data. The prediction was performed at base line where patients were still asymptomatic. Several image texture analysis and clinical features were used in order to create a classification model. The different features were statistically analyzed and we conclude that image texture analysis features extracted using Spatial Gray Level Dependencies method had the best statistical significance. Several predictive models were derived based on Binary Logistic Regression (BLR) and Support Vector Machines (SVM) modelling. The best results were obtained with the SVM modelling models with an average correct classifications score of 77±7% for differentiating between stroke event occurrences within 3 years versus more than 3 years. Further work is needed in investigating additional multiscale texture analysis features as well as more modelling techniques on more subjects