J. Miguel Sanches

Image reconstruction under multiplicative speckle noise using total variation

In this paper, we present a method for reconstructing images or volumes from a partial set of observations, under the Rayleigh distributed multiplicative noise model, which is the appropriate algebraic model in ultrasound (US) imaging. The proposed method performs a variable splitting to introduce an auxiliary variable to serve as the argument of the total variation (TV) regularizer term. Applying the Augmented Lagrangian framework and using an iterative alternating minimization method lead to simpler problems involving TV minimization with a least squares term. The resulting Gauss Seidel scheme is an instance of the Alternating Direction Method of Multipliers (ADMM) method for which convergence is guaranteed. Experimental results show that the proposed method achieves a lower reconstruction error than existing methods.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process.

Sleep and Wakefulness State Detection in Nocturnal Actigraphy Based on Movement Information

Wrist actigraphy (ACT) is a low-cost and well-established technique for long-term monitoring of human activity. It has a special relevance in sleep studies, where its noninvasive nature makes it a valuable tool for behavioral characterization and for the detection and diagnosis of some sleep disorders. The traditional sleep/ wakefulness state estimation algorithms from the nocturnal ACT data are unbalanced from a sensitivity and specificity points of view since they tend to overestimate sleep state, with severe consequences from a diagnosis point of view. They usually maximize the overall accuracy that does not take into account the highly unbalanced state distribution. In this paper, a method is proposed to appropriately deal with this unbalanced problem, achieving similar sensitivity and specificity scores in the state estimation process. The proposed method combines two linear discriminant classifiers, trained with two different criteria involving movement detection to generate a first state estimate. This result is then refined by a Hidden Markov Model-based algorithm. The global accuracy, the sensitivity, and the specificity of the method are 77.8 %, 75.6 %, and 81.6 %, respectively, performing better than the tested algorithms. If the performance is assessed only for movement periods, this improvement is even higher.

Hypnogram and Sleep Parameter Computation From Activity and Cardiovascular Data

The automatic computation of the hypnogram and sleep Parameters, from the data acquired with portable sensors, is a challenging problem with important clinical applications. In this paper, the hypnogram, the sleep efficiency (SE), rapid eye movement (REM), and nonREM (NREM) sleep percentages are automatically estimated from physiological (ECG and respiration) and behavioral (Actigraphy) nocturnal data. Two methods are described; the first deals with the problem of the hypnogram estimation and the second is specifically designed to compute the sleep parameters, outperforming the traditional estimation approach based on the hypnogram. Using an extended set of features the first method achieves an accuracy of 72.8%, 77.4%, and 80.3% in the detection of wakefulness, REM, and NREM states, respectively, and the second an estimation error of 4.3%, 9.8%, and 5.4% for the SE, REM, and NREM percentages, respectively.

Asymptomatic Carotid Disease—A New Tool for Assessing Neurological Risk

Active carotid plaques are associated with atheroembolism and neurological events; its identification is crucial for stroke prevention. High‐definition ultrasound (HDU) can be used to recognize plaque structure in carotid bifurcation stenosis associated with plaque vulnerability and occurrence of brain ischemic events. A new computer‐assisted HDU method to study the echomorphology of the carotid plaque and to determine a risk score for developing appropriate symptoms is proposed in this study. Plaque echomorphology characteristics such as presence of ulceration at the plaque surface, juxta‐luminal location of echolucent areas, echoheterogeneity were obtained from B‐mode ultrasound scans using several image processing algorithms and were combined with measurement of severity of stenosis to obtain a clinical score—enhanced activity index (EAI)—which was correlated with the presence or absence of ipsilateral appropriate ischemic symptoms. An optimal cutoff value of EAI was determined to obtain the best separation between symptomatic (active) from asymptomatic (inactive) plaques and its diagnostic yield was compared to other 2 reference methods by means of receiver‐operating characteristic (ROC) analysis. Classification performance was evaluated by leave‐one‐patient‐out cross‐validation applied to a cohort of 146 carotid plaques from 99 patients. The proposed method was benchmarked against (a) degree of stenosis criteria and (b) earlier proposed activity index (AI) and demonstrated that EAI yielded the highest accuracy up to an accuracy of 77% to predict asymptomatic plaques that developed symptoms in a prospective cross‐sectional study. Enhanced activity index is a noninvasive, easy to obtain parameter, which provided accurate estimation of neurological risk of carotid plaques.

Headset Bluetooth and cell phone based continuous central body temperature measurement system

The accurate measure of the central temperature is a very important physiologic indicator in several clinical applications, namely, in the characterization and diagnosis of sleep disorders. In this paper a simple system is described to continuously measure the body temperature at the ear. An electronic temperature sensor is coupled to the microphone of a common commercial auricular Bluetooth device that sends the temperature measurements to a mobile phone to which is paired. The measurements are stored at the mobile phone and periodically sent to a medical facility by email or SMS (short messaging service).

Global and local detection of liver steatosis from ultrasound

Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. Steatosis is usually a diffuse liver disease, since it is globally affected. However, steatosis can also be focal affecting only some foci difficult to discriminate. In both cases, steatosis is detected by laboratorial analysis and visual inspection of ultrasound images of the hepatic parenchyma. Liver biopsy is the most accurate diagnostic method but its invasive nature suggest the use of other non-invasive methods, while visual inspection of the ultrasound images is subjective and prone to error. In this paper a new Computer Aided Diagnosis (CAD) system for steatosis classification and analysis is presented, where the Bayes Factor, obatined from objective intensity and textural features extracted from US images of the liver, is computed in a local or global basis. The main goal is to provide the physician with an application to make it faster and accurate the diagnosis and quantification of steatosis, namely in a screening approach. The results showed an overall accuracy of 93.54% with a sensibility of 95.83% and 85.71% for normal and steatosis class, respectively. The proposed CAD system seemed suitable as a graphical display for steatosis classification and comparison with some of the most recent works in the literature is also presented.

Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

The usefulness of ultrasound in the classification of chronic liver disease

Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.

A CAD system for atherosclerotic plaque assessment

Recently, several atherosclerotic plaque characterization methods were proposed based on plaque morphology assessed through 2D ultrasound. It is of extreme importance to establish an objective quantification measure which allows the physicians to determine the risk of plaque rupture, and thus, of brain stroke. Having these, sometimes complex, measures easily and quickly assessed might prove invaluable for the physician an patient alike. This paper is a first attempt to incorporate such scores in a user-friendly software platform for Computer-aided Diagnosis. This tool provides a way to objectively and interactively characterize the atherosclerotic plaque, to store relevant patient data and to use several processing tools to outline the plaque and compute different echogenicity measures. Combinations of these features are used to provide two objective measure with clinical significance, known as activity index and enhanced activity index.