Denis Kouame

Spectral Correlation of the embolic blood Doppler signal

In a previous study we have shown that take into account the quasi-cyclostationary properties of the blood Doppler signal is useful to detect embolus (red blood cell aggregates). In this latter approach, we have first considered a simple off-line synchronous detector. As results were very interesting, we have thought that the correlation spectrum could be an interesting alternative to the synchronous detection. As the correlation spectrum of in vivo signal seems to be too complicated, we propose here to elucidate this apparent complexity by analytically computing the correlation spectrum of Doppler signal model with or without embolus

Adaptive AR and Neurofuzzy Approaches: Access to Cerebral Particle Signatures

In recent years, a relationship has been suggested between the occurrence of cerebral embolism and stroke. Ultrasound has therefore become essential in the detection of emboli when monitoring cerebral vascular disorders and forms part of ultrasound brain-imaging techniques. Such detection is based on investigating the middle cerebral artery using a TransCranial Doppler (TCD) system, and analyzing the Doppler signal of the embolism. Most of the emboli detected in practical experiments are large emboli because their signatures are easy to recognize in the TCD signal. However, detection of small emboli remains a challenge. Various approaches have been proposed to solve the problem, ranging from the exclusive use of expert human knowledge to automated collection of signal parameters. Many studies have recently been performed using time-frequency distributions and classical parameter modeling for automatic detection of emboli. It has been shown that autoregressive (AR) modeling associated with an abrupt change detection technique is one of the best methods for detection of microemboli. One alternative to this is a technique based on taking expert knowledge into account. This paper aims to unite these two approaches using AR modeling and expert knowledge through a neurofuzzy approach. The originality of this approach lies in combining these two techniques and then proposing a parameter referred to as score ranging from 0 to 1. Unlike classical techniques, this score is not only a measure of confidence of detection but also a tool enabling the final detection of the presence or absence of microemboli to be performed by the practitioner. Finally, this paper provides performance evaluation and comparison with an automated technique, i.e., AR modeling used in vitro

Multidimensional Complex Number Parametric Model Order and Parameters Estimation

This paper presents a technique for accessing multidimensional complex number AR model order and parameters through matrix factorization. The principle of this technique consists of the transformation of the multidimensional model to a pseudo SISO (simple input simple output) AR model then performing factorization of the covariance matrix of the data. This factorization then leads to a recursive form of the parameter and order estimation. The methodology developed here may be applied to an AR model of any dimension. Computer simulation results are provided to illustrate the behavior of this method

Synchronous Detection of Emboli by wavelet packet decomposition

We showed in a previous study that the detection of micro-emboli can be markedly improved by taking into account the quasi-cyclostationary properties of the blood Doppler signal. However, in order to detect still smaller events, we propose to combine the use of synchronous detectors and sub-band decomposition. We evaluated and compared the detectors performance to existing methods by using both numerical modeling of the Doppler signal and ROC curves.

Ultrasound imaging: signal acquisition, new advanced processing for biomedical and industrial applications

Use of ultrasound, namely in the biomedical diagnosis and industrial fields, pioneered in 1950s, is today particularly widespread. In the last decades, ultrasound imaging has benefited from advances in numerical technologies such as signal processing. On the other hand, the use of ultrasound imaging has increased the need for signal processing techniques. This paper presents a review and the up-to-date developments in ultrasound imaging techniques, including elementary principles, signal acquisition and processing, from one dimensional to multidimensional systems. This paper also deals with typical relevant applications.

Non-Stationary Parametric Spectral Estimation for Ultrasound Attenuation

Great progress1 has been made recently in high frequency ultrasound imaging. In particular, several works have shown the interest of echographic exploration for frequencies ranging from 20 to 100 MHz, in dermatology and ophthalmology. These results are extending the field cf application of tissue characterization. The measurement of acoustic attenuation tissues has received much interest in the field of ultrasound tissue characterization. Indeed, several clinical applications have shown a correlation between attenuation values and pathological states.