Aimé Lay-Ekuakille

Empirical Mode Decomposition vs. Wavelet Decomposition for the Extraction of Respiratory Signal From Single-Channel ECG: A Comparison

The respiratory signal can be accurately evaluated by single-channel electrocardiogram (ECG) processing, as shown in recent literature. Indirect methods to derive the respiratory signal from ECG can benefit from a simultaneous study of both respiratory and cardiac activities. These methods lead to major advantages such as low cost, high efficiency, and continuous noninvasive respiratory monitoring. The aim of this paper is to reconstruct the waveform of the respiratory signal by processing single-channel ECG. To achieve these goals, two techniques of decomposition of the ECG signal into suitable bases of functions are proposed, such as the empirical mode decomposition (EMD) and the wavelet analysis. The results highlight the main differences between them in terms of both theoretical foundations, and performance achieved by applying these algorithms to extract the respiratory waveform shape from single-channel ECG are presented. The results also show that both algorithms are able to reconstruct the respiratory waveform, although the EMD is able to break down the original signal without a preselected basis function, as it is necessary for wavelet decomposition. The EMD outperforms the wavelet approach. Some results on experimental data are presented.

Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains

Multimodal medical image fusion is effectuated to minimize the redundancy while augmenting the necessary information from the input images acquired using different medical imaging sensors. The sole aim is to yield a single fused image, which could be more informative for an efficient clinical analysis. This paper presents a two-stage multimodal fusion framework using the cascaded combination of stationary wavelet transform (SWT) and non sub-sampled Contourlet transform (NSCT) domains for images acquired using two distinct medical imaging sensor modalities (i.e., magnetic resonance imaging and computed tomography scan). The major advantage of using a cascaded combination of SWT and NSCT is to improve upon the shift variance, directionality, and phase information in the finally fused image. The first stage employs a principal component analysis algorithm in SWT domain to minimize the redundancy. Maximum fusion rule is then applied in NSCT domain at second stage to enhance the contrast of the diagnostic features. A quantitative analysis of fused images is carried out using dedicated fusion metrics. The fusion responses of the proposed approach are also compared with other state-of-the-art fusion approaches; depicting the superiority of the obtained fusion results.

A Robust Polynomial Filtering Framework for Mammographic Image Enhancement From Biomedical Sensors

This paper presents a non-linear framework employing a robust polynomial filter for accomplishing enhancement of mammographic abnormalities outcoming from biomedical instrumentation, i.e., X-rays instrumentation. The approach proposed in this paper uses a linear combination of Type-0 and Type-II polynomial filters as a generalized filtering solution to achieve enhancement of mammographic masses and calcifications irrespective of the nature of background tissues. A Type-0 filter provides contrast enhancement, suppressing the ill-effects of background noise. On the other hand, Type-II filter performs edge enhancement leading to preservation of finer details. Contrast improvement index is used as a performance measure to quantify the degree of improvement in contrast of the region-of interest. In addition, estimation of signal-to-noise ratio (in terms of PSNR and ASNR) is carried out to account for the suppression in background noise levels and over-enhancements of the processed mammograms. These measures are used as a mechanism to optimally select the filter parameters and also serve as a quantifying platform to compare the performance of the proposed filter with other non-linear enhancement techniques to be used for diverse biomedical image sensors.

Design and Characterization of a Nanocomposite Pressure Sensor Implemented in a Tactile Robotic System

In this paper, we present the implementation of a new class of optical pressure sensors in a robotic tactile-sensing system based on polydimethylsiloxane (PDMS). The sensor consists of a tapered optical fiber, where an optical signal goes across, embedded into a PDMS-gold nanocomposite material (GNM). By applying different pressure forces onto the PDMS-based nanocomposite, changes in the optical transmittivity of the fiber can be detected in real time due to the coupling between the GNM and the tapered fiber region. The intensity reduction of a transmitted light is correlated to the pressure force magnitude. Light intensity is converted into an electric signal by a system suitable for robotic implementation. High sensitivity using forces by applying weights of a few grams is proved. Sensitivity on the order of 5 g is checked. A detailed algorithm for the detection of roughness and shapes by means of a robotic finger is proposed.

Fully Automatic Segmentations of Liver and Hepatic Tumors From 3-D Computed Tomography Abdominal Images: Comparative Evaluation of Two Automatic Methods

An adaptive initialization method was developed to produce fully automatic processing frameworks based on graph-cut and gradient flow active contour algorithms. This method was applied to abdominal Computed Tomography (CT) images for segmentation of liver tissue and hepatic tumors. Twenty-five anonymized datasets were randomly collected from several radiology centres without specific request on acquisition parameter settings nor patient clinical situation as inclusion criteria. Resulting automatic segmentations of liver tissue and tumors were compared to their reference standard delineations manually performed by a specialist. Segmentation accuracy has been assessed through the following evaluation framework: dice similarity coefficient (DSC), false negative ratio (FNR), false positive ratio (FPR) and processing time. Regarding liver surfaces, graph-cuts achieved a DSC of 95.49% ( FPR=2.35% and FNR=5.10%), while active contours reached a DSC of 96.17% (FPR=3.35% and FNR=3.87%). The analyzed datasets presented 52 tumors: graph-cut algorithm detected 48 tumors with a DSC of 88.65%, while active contour algorithm detected only 44 tumors with a DSC of 87.10%. In addition, in terms of time performances, less time was requested for graph-cut algorithm with respect to active contour one. The implemented initialization method allows fully automatic segmentation leading to superior overall performances of graph-cut algorithm in terms of accuracy and processing time. The initialization method here presented resulted suitable and reliable for two different segmentation techniques and could be further extended.

A Polynomial filtering model for enhancement of mammogram lesions

This paper presents a preliminary analysis of a class of non-linear filters for enhancement of mammogram lesions. A non-linear filtering approach employing polynomial model of non-linearity is designed by second order truncation of Volterra series expansion. The proposed filter response is a linear combination of Type-0 and Type-II Volterra filters. Type-0 filter provides contrast enhancement, suppressing the ill-effects of background noise. On the other hand, Type-II filter employs edge enhancement. The objective analysis of the proposed filter is carried out by estimating values of quality parameters like CEM and PSNR on mammograms from MIAS and DDSM databases.

Entropy Index in Quantitative EEG Measurement for Diagnosis Accuracy

Electroencephalogram (EEG) remains the most immediate, simple, and rich source of information for understanding phenomena related to brain electrical activities. It is certainly a source of basic and interesting information to be extracted using specific and appropriate techniques. The most important aspect in processing EEG signals is to use less co-lateral assets and instrumentation in order to carried out a possible diagnosis; this is the approach of early diagnosis. Advanced estimate spectral analysis can reveal new information encompassed in EEG signals by means of specific parameters or indices. The research proposes a multidimensional approach with a combined use of decimated signal diagonalization (DSD) as basis from which it is possible to work by finding appropriate signal windows for revealing expected information and overcoming signal processing limitations encountered in quantitative EEG. Important information, about the state of the patient under observation, must be extracted from calculated DSD bispectrum. For this aim, it is useful to define an assessment index about the dynamic process associated with the analyzed signal. This information is measured by means of entropy, since the degree of order/disorder of the recorded EEG signal will be reflected in the obtained DSD bispectrum. The general advantage of multidimensional approach is to reveal eventual stealth frequencies “in space and in time” giving a topological vision to be correlated to physical areas which these frequencies emerge from. Long term and sleeping EEG recorded are analyzed, and the results obtained are of interest for an accurate diagnosis of the patients clinical condition.

Harmonic Ultrasound Imaging of Nanosized Contrast Agents for Multimodal Molecular Diagnoses

The aim of the present work was to demonstrate the possibility of selective detection of nanoparticle contrast agents (NPCAs) on diagnostic echographic images by exploiting the second harmonic component they introduce in the spectra of corresponding ultrasound signals, as a consequence of nonlinear distortion during ultrasound propagation. We employed silica nanospheres (SiNSs) of variable diameter (160 nm, 330 nm, and 660 nm) dispersed in different volume concentrations (range 0.07-0.8%) in agarose gel samples that were automatically scanned through a digital ecograph using narrow-band ultrasound pulses at 6.6 MHz and variable mechanical index (MI range 0.2-0.6). In the first part of the study, the intensity peaks of four different spectral components of the backscattered signal were considered: fundamental (detected in correspondence of the incident ultrasound frequency), subharmonic (detected at half of the fundamental frequency), ultra harmonic (detected at 1.5 times the fundamental frequency), and second harmonic (detected at twice the fundamental frequency). Subsequently, based on the experimental results of the first part of the study and on our recently reported findings, the focus was moved to a detailed comparison between subharmonic and second harmonic trend, which were determined as a function of nanoparticle composition, sample concentration, and MI. The experiments were also repeated on different agarose samples, containing SiNSs covered by an outer shell of smaller magnetic nanoparticles, made of either iron oxide (IO) or FePt-IO nanocrystals. Obtained results show that this new ultrasound-based method for NPCA imaging has a detection sensitivity similar to that of our previously introduced subharmonic-based technique in the presence of 330-nm SiNSs, but performs significantly better in the detection of both the types of “dual mode” NPCAs. The fact that the reported detection method was optimized for identification of 330-nm SiNSs (a sort of “ideal” size for the development of novel tumor-targeting NPCAs) and that the magnetically coated particles are detectable also through magnetic resonance imaging makes the presented second harmonic ultrasound method a valuable solution for the introduction of new protocols for multimodal molecular diagnoses employing only nonionizing radiations.

A Composite Wavelets and Morphology Approach for ECG Noise Filtering

Noisy ECG signals contain variations in the amplitudes or in the time intervals which represents the abnormalities associated with the heart; thereby making visual diagnosis difficult for cardiovascular diseases. Hence, to facilitate proper analysis of ECG; this paper presents a combination of wavelets analysis and morphological filtering as an approach for noise removal in ECG signals. The proposed algorithm involves sub-band decomposition of ECG signal using bi-orthogonal wavelet family. The wavelet detail coefficients containing the noisy components are then processed by morphological operators using linear structuring elements. The morphological filter processes only the corrupted bands without affecting the signal parameters. Simulation results of the proposed algorithm show noteworthy suppression of noise in terms of higher signal-to-noise ratio preserving the ST segment and R wave of ECG.

Robust Spectral Leak Detection of Complex Pipelines Using Filter Diagonalization Method

The control and managing of pipelines have been assuming a major importance for all kinds of fluids to be conveyed through. When the fluid is like oil, harmful liquid and/or water for human beings necessity, the monitoring of pipelines becomes extremely fundamental. Based on the reflexion according to fast detecting systems, spectral analysis response is a topic of interest. Among spectral analysis response techniques, fast Fourier transform (FFT) is rated. Different other techniques are utilized, but they are costly and difficult to be used. An interesting technique, used in nuclear magnetic resonance data processing, filter diagonalization method (FDM), for tackling FFT limitations, can be used, by considering the pipeline, especially complex configurations, as a vascular apparatus with arteries, veins, capillaries, etc. The thrombosis, for human vascular apparatus, that might be occur, can be considered as a leakage for the complex pipeline. The research proposes the use of FDM according to two sub techniques called algorithm I and algorithm II. The first algorithm is a direct transformation of FDM application, while the second includes robustness and a regularization technique to solve ill-posed problems that may emerge in processing data. The results are encouraging.