João Luiz Azevedo de Carvalho

Left ventricle segmentation in echocardiography using a radial-search-based image processing algorithm

A new left ventricle segmentation method in two-dimensional echocardiography images is proposed. Image processing techniques combined with radial search and temporal information are used to extract the left ventricle boundary. Borders from sequential images are extracted using the proposed method, and a curve illustrating the area variation within a cardiac cycle is presented. Performance evaluation is performed by comparing the borders obtained from the presented method to those manually prescribed by a medical specialist. The new sequential radial search algorithm improved the border extraction from long-axis ultrasound images, specially the ones where the mitral valve was open. Segmentation errors due to low contrast were corrected.

A tool for time-frequency analysis of heart rate variability

The analysis of heart rate variability (HRV) signals is an important tool for studying the autonomic nervous system, as it allows the evaluation of the balance between the sympathetic and parasympathetic influences on heart rhythm. Time-frequency analysis of HRV makes it easier to evaluate how this balance varies with time. This work presents a tool for time-frequency analysis of heart rate variability (HRV) developed in Matlab 6.5. Three techniques are available: Short-Time Fourier Transform, Continuous Wavelet Transform Scalogram and Time-Variant Auto-Regressive Modeling.

Estimation of the knee joint angle from surface electromyographic signals for active control of leg prostheses

The surface electromyographic (SEMG) signal is very convenient for prosthesis control because it is non-invasively acquired and intrinsically related to the users intention. This work presents a feature extraction and pattern classification algorithm for estimation of the intended knee joint angle from SEMG signals acquired using two sets of electrodes placed on the upper leg. The proposed algorithm uses a combination of time-domain and frequency-domain approaches for feature extraction (signal amplitude histogram and auto-regressive coefficients, respectively), a self-organizing map for feature projection and a Levenberg-Marquardt multi-layer perceptron neural network for pattern classification. The new algorithm was quantitatively compared with the method proposed by Wang et al (2006 Med. Biol. Eng. Comput. 44 865-72), which uses wavelet packet feature extraction, principal component analysis and a multi-layer perceptron neural classifier. The proposed method provided lower error-to-signal percentage and peak error amplitudes, higher correlation and fewer error events. The algorithm presented in this work may be useful as part of a myoelectric controller for active leg prostheses designed for transfemoral amputees.

Semi-automatic detection of the left ventricular border

Two semi-automatic methods for the detection of the left ventricular border in two-dimensional short axis echocardiographic images are presented and compared. In these methods, the left ventricular area variation curve is calculated during a complete cardiac cycle after the segmentation of several frames. This allows the evaluation of the cardiovascular dynamics and the identification of important clinical parameters. The algorithms are proposed as several independent modules. The results are validated through the comparison between the semi-automatic continuous boundaries and manuals boundaries sketched by a medical specialist.

Feasibility of in vivo measurement of carotid wall shear rate using spiral fourier velocity encoded MRI

Arterial wall shear stress is widely believed to influence the formation and growth of atherosclerotic plaque; however, there is currently no gold standard for its in vivo measurement. The use of phase contrast MRI has proved to be challenging due to partial‐volume effects and inadequate signal‐to‐noise ratio at the high spatial resolutions that are required. This work evaluates the use of spiral Fourier velocity encoded MRI as a rapid method for assessing wall shear rate in the carotid arteries. Wall shear rate is calculated from velocity histograms in voxels spanning the blood/vessel wall interface, using a method developed by Frayne and Rutt (Magn Reson Med 1995;34:378–387). This study (i) demonstrates the accuracy of the velocity histograms measured by spiral Fourier velocity encoding in a pulsatile carotid flow phantom compared with high‐resolution two‐dimensional Fourier transform phase contrast, (ii) demonstrates the accuracy of Fourier velocity encoding–based shear rate measurements in a numerical phantom designed using a computational fluid dynamics simulation of carotid flow, and (iii) demonstrates in vivo measurement of regional wall shear rate and oscillatory shear index in the carotid arteries of healthy volunteers at 3 T. Magn Reson Med 63:1537–1547, 2010.

Rapid Quantitation of Cardiovascular Flow Using Slice-Selective Fourier Velocity Encoding with Spiral Readouts

Accurate flow visualization and quantitation is important for the assessment of many cardiovascular conditions such as valvular stenosis and regurgitation. Phase contrast based methods experience partial volume artifacts when flow is highly localized, complex and/or turbulent. Fourier velocity encoding (FVE) avoids such problems by resolving the full velocity distribution within each voxel. This work proposes the use of slice selective FVE with spiral readouts to acquire fully localized velocity distributions in a short breath‐hold. Scan‐plane prescription is performed using classic protocols, and an automatic algorithm is used for in‐plane localization of the flow. Time and spatially‐resolved aortic valve velocity distributions with 26‐msec temporal resolution and 25 cm/sec velocity resolution over a 600 cm/sec field‐of‐view were acquired in a 12‐heartbeat breath‐hold. In carotid studies, scan time was extended to achieve higher spatial resolution. The method was demonstrated in healthy volunteers and patients, and the results compared qualitatively well with Doppler ultrasound. Acquisition time could be reduced to 7 heartbeats (a 42% reduction) using partial Fourier reconstruction along the velocity dimension. Magn Reson Med 57:639–646, 2007.

A prospective non-randomised study to compare oral trauma from laryngoscope versus laryngeal mask insertion

BACKGROUND Prospective studies evaluating the incidence of dental injuries in anaesthesia are scarce or absent. The aim of this study was to compare the incidence of oral trauma in patients submitted to laryngoscopy and orotracheal intubation with those anaesthetized with a laryngeal mask. MATERIAL AND METHODS This observational study was performed in the University Hospital, with blind evaluation. We evaluated 121 patients older than 18 years old who were submitted to elective surgery under general anaesthesia. Patients were excluded if they were pregnant, underwent surgery of the mouth or required nasal intubation. Laryngoscopy and orotracheal tube were used in 70 patients and laryngeal mask in 51. Twelve to 24 h before anaesthesia and after surgery, all patients underwent a detailed oral examination performed by an anaesthesiology and a senior dentist, both blind to anaesthetic management details. Injuries of the teeth were diagnosed based on WHOs classification system modified by Andreasen. RESULTS Oral injuries were found in 84.1% of the patients after laryngoscopy and 19.6% after laryngeal mask insertion (P < 0.001). Corresponding values for teeth injuries were, respectively, 38.6% and 2.0% (P < 0.001). The great majority were enamel fractures of the maxillary incisors. This means that patients submitted to laryngoscopy had a significantly higher incidence of oral injuries compared with those having laryngeal mask insertion (unadjusted OR 21, 99; CI 0.95: 8.55-56.55). CONCLUSION Minor oral trauma is significantly more frequent after endotracheal intubation than after use of the laryngeal mask. This is true for injuries of the teeth, inferior lip and tongue. Further studies are needed to evaluate on a long-term basis the clinical relevance of the dental injuries we found.

A New Wavelet-Based Algorithm for Compression of Emg Signals

Despite the growing interest in the transmission and storage of electromyographic signals for long periods of time, only a few studies dealt with the compression of these signals. In this article we propose a novel algorithm for EMG signal compression using the wavelet transform. For EMG signals acquired during isometric contractions, the proposed algorithm provided compression factors ranging from 50 to 90%, with an average PRD ranging from 1.4 to 7.5%. The proposed method uses a new scheme for normalizing the wavelet coefficients. The wavelet coefficients are quantized using dynamic bit allocation, which is carried out by a Kohonen Neural Network. After the quantization, these coefficients are encoded using an arithmetic encoder. The compression results using the proposed algorithm were compared to other algorithms based on the wavelet transform. The proposed algorithm had a better performance in compression ratio and fidelity of the reconstructed signal.

Improved segmentation of echocardiographic images using fusion of images from different cardiac cycles

In this work, an algorithm for the detection of the left ventricular border in two-dimensional long axis echocardiographic images is presented. In its preprocessing stage, images fusion was applied to a sequence of images composed of three cardiac cycles. This method exploits the similarity of corresponding frames from different cycles and produces contrast enhancement in the left ventricular boundary. This result improves the performance of the segmentation stage which is based on watershed transformation. The obtained left ventricle border is quantitatively and qualitatively compared with contours manually segmented by a cardiologist, and with results obtained using seven different techniques from the literature.

Compression of electromyographic signals using image compression techniques

Despite the growing interest in the transmission and storage of electromyographic signals for long periods of time, few studies have addressed the compression of such signals. In this article we present an algorithm for compression of electromyographic signals based on the JPEG2000 coding system. Although the JPEG2000 codec was originally designed for compression of still images, we show that it can also be used to compress EMG signals for both isotonic and isometric contractions. For EMG signals acquired during isometric contractions, the proposed algorithm provided compression factors ranging from 75 to 90%, with an average PRD ranging from 3.75% to 13.7%. For isotonic EMG signals, the algorithm provided compression factors ranging from 75 to 90%, with an average PRD ranging from 3.4% to 7%. The compression results using the JPEG2000 algorithm were compared to those using other algorithms based on the wavelet transform.