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Recent studies have suggested that textural characteristics of the intima-media complex (IMC) may be more useful than the intima-media thickness (IMT) in evaluating cardiovascular risk. The primary aim of our study was to investigate the association between texture features of the common carotid IMC and prevalent clinical cardiovascular disease (CVD). The secondary aim was to determine whether IMT and IMC texture features vary between the left and right carotid arteries. The study was performed on 2208 longitudinal-section ultrasound images of the left (L) and right (R) common carotid artery (CCA), acquired from 569 men and 535 women out of which 125 had clinical CVD. L and R sides of the IMC were intensity normalized and despeckled. The IMC was semiautomatically delineated for all images using a semiautomated segmentation system, and 61 different texture features were extracted. The corresponding IMT semiautomated measurements (mean±SD) of the L and R sides were 0.73±0.21 mm/0.69±0.19 mm for the normal population and 0.83±0.17 mm/0.79±0.18 mm for those with CVD. IMC texture features did not differ between the right- and left-hand sides. Several texture features were independent predictors of the presence of CVD. The multivariate logistic regression analysis combining age, IMT, and texture features produced a receiver operating characteristic curve with an area under the curve of 89%. A correct classification rate of 77% for separating the normal subject (NOR) versus CVD subjects was achieved using the support vector machine classifier with a combination of clinical features, IMT, and extracted texture features. Texture features provide additional information on the presence of clinical CVD, which is over and above that provided by conventional risk factors or IMT alone. The value of IMC texture features in the prediction of future cardiovascular events should be tested in prospective studies. [ABSTRACT FROM AUTHOR]

• This study proposes and evaluates an automated integrated speech signal and face image analysis system for the identification of seven different human emotions. • Signal speech and face texture features were extracted from free speech recordings and face images. • Statistically significantly different speech and face image features were identified that may be used to distinguish between all different human emotions. • An overall percentage of correct classification (%CC) score of 93% was achieved. • Future work will investigate the development and integration of the proposed method into a mobile device. Human interactions are related to speech and facial characteristics. It was suggested that speech signals and/or images of facial expressions may reveal human emotions and that both interact for the verification of a person's identity. The present study proposes and evaluates an automated integrated speech signal and facial image analysis system for the identification of seven different human emotions (Normal (N), Happy (H), Sad (S), Disgust (D), Fear (F), Anger (A), and Surprise (Su)). Speech recordings and face images from 7,441 subjects aged 20≤age≤74 were collected, normalized and filtered. From all the above recordings 55 speech signal features and 61 different image face texture features were extracted. Statistical and model multi-classification analysis were performed to select the features able to statistically significantly distinguish between the seven aforementioned human emotions (N, H, S, D, F, A and Su). The selected features alone or a combination of these features along with age and gender of the sample investigated were used to build two learning-based classifiers; and the classifiers' accuracy was computed. For each of the above mentioned human emotions, statistical significantly different speech and face image features were identified that may be used to distinguish between the aforementioned groups (N vs H, N vs S, N vs D, N vs F, N vs A, N vs Su). Using solely the statistically significant speech and image features identified, an overall percentage of correct classification (%CC) score of 93% was achieved. A significant number of speech and face image features have been derived from continuous speech and face images. Features were identified that were able to identify between seven different emotional human states. This study poses the basis for the development of an integrated system for the identification of emotional states from automatic analysis of free speech and image face analysis. Future work will investigate the development and integration of the proposed method into a mobile device. [ABSTRACT FROM AUTHOR]

There is a clinical interest in identifying normal appearing white matter (NAWM) areas in brain T2-weighted (T 2 W) MRI scans in multiple sclerosis (MS) subjects. These areas are susceptible to disease development and areas need to be studied in order to find potential associations between texture feature changes and disease progression. The subjects investigated had a first demyelinating event (Clinically Isolated Syndrome-CIS) at baseline (Time 0), and the NAWM 0 (i.e. NAWM at Time 0) of the brain tissue was subsequently converted to demyelinating plaques (as evaluated in a follow up MRI at Time 6 – 12). 38 untreated subjects that had developed a CIS, had brain MRI scans within an interval of 6–12 months (Time 6 – 12 at follow-up). An experienced MS neurologist manually delineated the demyelinating lesions at Time 0 (L 0) and at Time 6 – 12 (L 6 – 12). Areas in the Time 6 – 12 MRI scans, where new lesions had been developed, were mapped back to their corresponding NAWM areas on the Time 0 MR scans (ROIS 0). In addition, contralateral ROIs of similar size and shape were segmented on the same images at Time 0 (ROIS C0) to form an intra-subject control group. Following that, texture features were extracted from all prescribed areas and MS lesions. Texture features were used as input into Support Vector Machine (SVM) models to differentiate between the following: NAWM 0 vs ROIS C0 , NAWM 0 vs NAWM 6 – 12 , NAWM 0 vs L 0 , NAWM 6 – 12 vs L 6 – 12 , ROIS 0 vs L 0 , ROIS 0 vs L 6 – 12 and ROIS 0 vs ROIS C0 , where the corresponding % correct classifications scores were 89%, 95%, 98%, 92%, 85%, 90% and 65% respectively. Texture features may provide complementary information for following up the development and progression of MS disease. Future work will investigate the proposed method on more subjects. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate differences in intima-media thickness (IMT) and diameter (D) measurements of the common carotid artery (CCA) in ultrasound imaging in normal subjects and renal failure disease (RFD) patients. Manual measurements by two experts and automated segmentation measurements (based on snakes and active contour models (ACM)) were carried out on 73 normal subjects, and 80 RFD patients. Statistical analysis was carried out using the Wilcoxon rank-sum test at p<0.05. Results demonstrated that the mean IMT and D measurements were significantly higher for the RFD group versus the normal group. Moreover, there was no significant difference between the manual and automated measurements. The ACM segmentation was slightly more accurate than segmentation based on snakes. Further work is needed to validate these findings on a larger group of subjects. [ABSTRACT FROM AUTHOR]

Abstract: Ultrasound imaging of the common carotid artery (CCA) is a non-invasive tool used in medicine to assess the severity of atherosclerosis and monitor its progression through time. It is also used in border detection and texture characterization of the atherosclerotic carotid plaque in the CCA, the identification and measurement of the intima-media thickness (IMT) and the lumen diameter that all are very important in the assessment of cardiovascular disease (CVD). Visual perception, however, is hindered by speckle, a multiplicative noise, that degrades the quality of ultrasound B-mode imaging. Noise reduction is therefore essential for improving the visual observation quality or as a pre-processing step for further automated analysis, such as image segmentation of the IMT and the atherosclerotic carotid plaque in ultrasound images. In order to facilitate this preprocessing step, we have developed in MATLAB® a unified toolbox that integrates image despeckle filtering (IDF), texture analysis and image quality evaluation techniques to automate the pre-processing and complement the disease evaluation in ultrasound CCA images. The proposed software, is based on a graphical user interface (GUI) and incorporates image normalization, 10 different despeckle filtering techniques (DsFlsmv, DsFwiener, DsFlsminsc, DsFkuwahara, DsFgf, DsFmedian, DsFhmedian, DsFad, DsFnldif, DsFsrad), image intensity normalization, 65 texture features, 15 quantitative image quality metrics and objective image quality evaluation. The software is publicly available in an executable form, which can be downloaded from http://www.cs.ucy.ac.cy/medinfo/. It was validated on 100 ultrasound images of the CCA, by comparing its results with quantitative visual analysis performed by a medical expert. It was observed that the despeckle filters DsFlsmv, and DsFhmedian improved image quality perception (based on the expert's assessment and the image texture and quality metrics). It is anticipated that the system could help the physician in the assessment of cardiovascular image analysis. [Copyright &y& Elsevier]

The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of the development of cardiovascular disease (CVD). It was proposed but not thoroughly investigated that the media layer (ML) thickness (MLT), its composition, and its texture may be indicative of cardiovascular risk and for differentiating between patients with high and low risk. In this study, we investigate an automated method for segmenting the ML and the intima layer (IL) and measurement of the MLT and the intima layer thickness (ILT) in ultrasound images of the CCA. The snakes segmentation method was used and was evaluated on 100 longitudinal ultrasound images acquired from asymptomatic subjects, against manual segmentation performed by a neurovascular expert. The mean ± standard deviation (Sd) for the first and second sets of manual and the automated IMT, MLT, and ILT measurements were 0.71 ± 0.17 mm, 0.72 ± 0.17 mm, 0.67 ± 0.12 mm; 0.25 ± 0.12 mm, 0.27 ± 0.14 mm, 0.25 ± 0.11 mm; and 0.43 ± 0.10 mm, 0.44 ± 0.13 mm, and 0.42 ± 0.10 mm, respectively. There was overall no significant difference between the manual and the automated IMC, ML, and IL segmentation measurements. Therefore, the automated segmentation method proposed in this study may be used successfully in the measurement of the MLT and ILT complementing the manual measurements. MLT was also shown to increase with age (for both the manual and the automated measurements). Future research will incorporate the extraction of texture features from the segmented ML and IL bands, which may indicate the risk of future cardiovascular events. However, more work is needed for validating the proposed technique in a larger sample of subjects. [ABSTRACT FROM AUTHOR]

It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and teleconsultation. The objective of this work was to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts in the assessment of 440 (220 asymptomatic and 220 symptomatic) ultrasound images of the carotid artery bifurcation. In this paper a total of 10 despeckle filters were evaluated based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. The results of this study suggest that the first order statistics filter lsmv, gave the best performance, followed by the geometric filter gf4d, and the homogeneous mask area filter lsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. More specifically, filters lsmv or gf4d can be used for despeckling asymptomatic images in which the expert is interested mainly in the plaque composition and texture analysis; and filters lsmv, gf4d, or lsminsc can be used for the despeckling of symptomatic images in which the expert is interested in identifying the degree of stenosis and the plaque borders. The proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging. [ABSTRACT FROM AUTHOR]

• Diaphragmatic muscle motion characteristics may provide useful information about diaphragmatic function. • Diaphragmatic motion semi-automated and manual measurements are not statistically significantly different. • The diaphragmatic excursion derived in this study for the subjects investigated at risk or had presumed diaphragmatic weakness was significantly lower than that of normal subjects based on published literature. • Diaphragmatic ultrasound video recorded in the course of a B-mode examination can be used to generate an M-mode image where diaphragmatic motion measurements can be performed. • The usefulness of the proposed system still remains to be evaluated in the clinical practice in the assessment and follow up of patients with diaphragmatic weakness or paralysis. Ultrasound diaphragmatic muscle motion characteristics may provide useful information about normal or abnormal diaphragmatic function and indicate diaphragmatic weakness, or paralysis. In the present work we propose and evaluate an integrated semi-automated analysis system for the quantitative analysis of ultrasonic motion from ultrasound diaphragmatic videos. The proposed system was evaluated in simulated videos and in 13 patients, four of whom patients were mechanically ventilated. The major steps of the methodology were as follows: video normalization, despeckle filtering, generation of an M-Mode image, snakes segmentation, and motion measurements. The following manual (-/) vs semi-automated (/-), (median±IQR) measurements, which are routinely carried out by the experts, for assessing the severity of the disease, were computed. For the simulated videos the diaphragmatic excursion was 1.80±0.00 cm / 1.76±0.03 cm. For all the real ultrasound videos investigated in this study the following measurements were computed: (i) diaphragmatic excursion: 0.84±0.15 cm / 0.83±0.14 cm, (ii) inspiration time (T insp): 0.71±0.18 sec / 0.70±0.15 sec, (iii) total breathing time for one cycle (T tot): 1.71±0.37 sec / 1.67±0.37 sec, (iv) diaphragmatic curve slope: 1.29±0.36 cm/sec / 1.27±0.36 cm/sec, and (v) relaxation rate (RR): 0.82±0.17 cm/sec / 0.82±0.18 cm/sec. Manual and semi-automated measurements were very close with non-statistical significant differences and strong correlations between them. It is anticipated that the proposed system might be useful in the clinical practice in the assessment and follow up of patients with diaphragmatic weakness or paralysis and aid in the separation of normal and abnormal diaphragmatic motion. Further validation and additional experimentation in a larger sample of videos and different patient groups is required. [ABSTRACT FROM AUTHOR]

Common carotid intima-media thickness (CIMT) is a commonly used marker for atherosclerosis and is often computed in carotid ultrasound images. An analysis of different computerized techniques for CIMT measurement and their clinical impacts on the same patient data set is lacking. Here we compared and assessed five computerized CIMT algorithms against three expert analysts' manual measurements on a data set of 1088 patients from two centers. Inter- and intra-observer variability was assessed, and the computerized CIMT values were compared with those manually obtained. The CIMT measurements were used to assess the correlation with clinical parameters, cardiovascular event prediction through a generalized linear model and the Kaplan-Meier hazard ratio. CIMT measurements obtained with a skilled analyst's segmentation and the computerized segmentation were comparable in statistical analyses, suggesting they can be used interchangeably for CIMT quantification and clinical outcome investigation. To facilitate future studies, the entire data set used is made publicly available for the community at http://dx.doi.org/10.17632/fpv535fss7.1. [ABSTRACT FROM AUTHOR]