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Dive into the research topics where M.G. Ruano is active.

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Featured researches published by M.G. Ruano.


IEEE Transactions on Biomedical Engineering | 2008

A Soft-Computing Methodology for Noninvasive Time-Spatial Temperature Estimation

César Alexandre Teixeira; M.G. Ruano; A. E. Ruano; W. C. A. Pereira

The safe and effective application of thermal therapies is restricted due to lack of reliable noninvasive temperature estimators. In this paper, the temporal echo-shifts of backscattered ultrasound signals, collected from a gel-based phantom, were tracked and assigned with the past temperature values as radial basis functions neural networks input information. The phantom was heated using a piston-like therapeutic ultrasound transducer. The neural models were assigned to estimate the temperature at different intensities and points arranged across the therapeutic transducer radial line (60 mm apart from the transducer face). Model inputs, as well as the number of neurons were selected using the multiobjective genetic algorithm (MOGA). The best attained models present, in average, a maximum absolute error less than 0.5 C, which is pointed as the borderline between a reliable and an unreliable estimator in hyperthermia/diathermia. In order to test the spatial generalization capacity, the best models were tested using spatial points not yet assessed, and some of them presented a maximum absolute error inferior to 0.5 C, being ldquoelectedrdquo as the best models. It should be also stressed that these best models present implementational low-complexity, as desired for real-time applications.


Ultrasonics | 2010

Influence of temperature variations on the entropy and correlation of the Grey-Level Co-occurrence Matrix from B-Mode images

André V. Alvarenga; César Alexandre Teixeira; M.G. Ruano; W. C. A. Pereira

In this work, the feasibility of texture parameters extracted from B-Mode images were explored in quantifying medium temperature variation. The goal is to understand how parameters obtained from the gray-level content can be used to improve the actual state-of-the-art methods for non-invasive temperature estimation (NITE). B-Mode images were collected from a tissue mimic phantom heated in a water bath. The phantom is a mixture of water, glycerin, agar-agar and graphite powder. This mixture aims to have similar acoustical properties to in vivo muscle. Images from the phantom were collected using an ultrasound system that has a mechanical sector transducer working at 3.5 MHz. Three temperature curves were collected, and variations between 27 and 44 degrees C during 60 min were allowed. Two parameters (correlation and entropy) were determined from Grey-Level Co-occurrence Matrix (GLCM) extracted from image, and then assessed for non-invasive temperature estimation. Entropy values were capable of identifying variations of 2.0 degrees C. Besides, it was possible to quantify variations from normal human body temperature (37 degrees C) to critical values, as 41 degrees C. In contrast, despite correlation parameter values (obtained from GLCM) presented a correlation coefficient of 0.84 with temperature variation, the high dispersion of values limited the temperature assessment.


ieee international symposium on intelligent signal processing, | 2007

NARX structures for non-invasive temperature estimation in non-homogeneous media

C. A. Teixeira; W. C. A. Pereira; A. E. Ruano; M.G. Ruano

The safe and effective application of thermal therapies are limited by the existence of precise non-invasive temperature estimators. Such estimators would enable a correct power deposition on the region of interest by means of a correct instrumentation control. In multi-layered media, the temperature should be estimated at each layer and especially at the interfaces, where significant temperature changes should occur during therapy. In this work, a non-linear autoregressive structure with exogenous inputs (NARX) was applied to non-invasively estimate temperature in a multi-layered (non-homogeneous) medium, while submitted to physiotherapeutic ultrasound. The NARX structure is composed by a static feed-forward radial basis functions neural network (RBFNN), with external dynamics induced by its inputs. The NARX structure parameters were optimized by means of a multi-objective genetic algorithm. The best attained models reached a maximum absolute error inferior to 0.5degC (proposed threshold in hyperthermia/diathermia) at both the interface and inner layer points, at four radiation intensities. These models present also a small computational complexity as desired for real-time applications. To the best of ours knowledge this is the first non-invasive estimation approach in multi-layered media using ultrasound for both heating and estimation.


IFAC Proceedings Volumes | 1999

A time-frequency spectral implementation for a real time biomedical application

M.M. Madeira; M. O. Tokhi; M.G. Ruano

Abstract Doppler ultrasound cardiovascular disease detection is commonly performed using signal processing techniques. Among the time-frequency distributions (TFDs) the Choi-Williams distribution (CW) provides a substantial improvement in the estimation of some selected parameters for clinical diagnosis in comparison to the classical Short Term Fourier Transform (STFT) (Cardoso et al., 1996a). Due to the computational burden involved in CW estimation, development of strategies for an efficient real-time implementation is required. This paper presents an investigation into the real time implementation of CW spectral estimator using transputers and digital signal processing (DSPs) devices. Results of implementation of the algorithm on the architectures are presented and discussed.


soft computing | 2013

On the Use of Artificial Neural Networks for Biomedical Applications

M.G. Ruano; A. E. Ruano

Artificial Neural Networks (ANN) are being extensively used in many application areas due to their ability to learn and generalize from data, similarly to a human reaction. This paper reports the use of ANN as a classifier, dynamic model, and diagnosis tool. The examples presented include blood flow emboli classification based on transcranial ultrasound signals, tissue temperature modeling based on imaging transducer’s raw data and identification of ischemic cerebral vascular accident areas based on computer tomography images. In all case studies the performance of ANN proves to produce very accurate results, encouraging the more frequent use of these computational intelligent techniques on medical applications.


ieee international symposium on intelligent signal processing, | 2007

Neural networks assisted diagnosis of ischemic CVA's through CT scan

Luís Ribeiro; A. E. Ruano; M.G. Ruano; Pedro M. Ferreira

Technological and computing evolution promoted new opportunities to improve the quality of life through new medical achievements, in particular, the quality of diagnostic evaluations. Computerised tomography (CT) is one of the imaging equipments for diagnosis which has most benefited from technological improvements. Because of that, and due to the quality of the diagnosis produced, it is one of the most employed equipments in clinical applications. As an example, the ischaemic cerebral vascular accident (ICVA) is a pathology confirming the frequent use of CT. The interest in this pathology, and in general for the encephalon image analysis as a preventive diagnosis, is mainly due to its frequent occurrence in development countries and its social- economic impact. In this paper we propose to evaluate the ability of artificial neural networks (ANNs) for automatic identification of ICVAs by means of tissue density images obtained by CT. Cranioencephalon CT exams and their respective medical reports were used to train ANN classifiers by means of features extracted from the images. Once the ANNs were trained, the classifiers were tested with data never seen by the network. At this stage we may conclude that the ANNs may significantly contribute as an ICVAs CT diagnostic aid, since among the test cases the automatic identification of ischaemic lesions has been performed with no false negatives and very few false positives.


Computer Methods and Programs in Biomedicine | 2017

An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images

Elmira Hajimani; M.G. Ruano; A. E. Ruano

OBJECTIVE This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images. METHODS For the design of a neural network classifier, a Multi Objective Genetic Algorithm (MOGA) framework is used to determine the architecture of the classifier, its corresponding parameters and input features by maximizing the classification precision, while ensuring generalization. This approach considers a large number of input features, comprising first and second order pixel intensity statistics, as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line. RESULTS Values of specificity of 98% and sensitivity of 98% were obtained, at pixel level, by an ensemble of non-dominated models generated by MOGA, in a set of 150 CT slices (1,867,602pixels), marked by a NeuroRadiologist. This approach also compares favorably at a lesion level with three other published solutions, in terms of specificity (86% compared with 84%), degree of coincidence of marked lesions (89% compared with 77%) and classification accuracy rate (96% compared with 88%).


soft computing | 2013

On-line operation of an intelligent seismic detector

Guilherme Madureira; A. E. Ruano; M.G. Ruano

This study describes the on-line operation of a seismic detection system to act at the level of a seismic station providing similar role to that of a STA / LTA ratio- based detection algorithms. The intelligent detector is a Support Vector Machine (SVM), trained with data consisting of 2903 patterns extracted from records of the PVAQ station, one of the seismographic network’s stations of the Institute of Meteorology of Portugal (IM). Records’ spectral variations in time and characteristics were reflected in the SVM input patterns, as a set of values of power spectral density at selected frequencies. To ensure that all patterns of the sample data were within the range of variation of the training set, we used an algorithm to separate the universe of data by hyper-convex polyhedrons, determining in this manner a set of patterns that have a mandatory part of the training set. Additionally, an active learning strategy was conducted, by iteratively incorporating poorly classified cases in the training set. After having been trained, the proposed system was experimented in continuous operation for unseen (out of sample) data, and the SVM detector obtained 97.7% and 98.7% of sensitivity and selectivity, respectively. The same type of ANN presented 88.4 % and 99.4% of sensitivity and selectivity when applied to data of a different seismic station of IM.


SOFA | 2013

Characterization of Temperature-Dependent Echo-Shifts and Backscattered Energy Induced by Thermal Ultrasound

M.G. Ruano; César Alexandre Teixeira; Javid J. Rahmati

Existence of accurate temporal-spatial temperature models, which would enable non-invasive estimates, will promote ultrasound-based thermal therapy applications. These models should reflect the tissue temperature with a maximum absolute error of 0.5 oC within 1 cm3.


international conference of the ieee engineering in medicine and biology society | 2001

Neural network classification of cerebral embolic signals

S. Matos; M.G. Ruano; A. E. Ruano; D.H. Evans

The presence of circulating cerebral emboli represents an increased risk of stroke. The detection of such emboli is possible with the use of a transcranial Doppler ultrasound (TCD) system. When a gaseous or particulate embolus passes through the TCD sample volume, it produces high intensity transient signals that are normally relatively easily detected. However, because most current TCD systems rely on human experts for the detection and classification of candidate events, this technique is not widely used. The appearance of a reliable automatic system, able to detect these signals and to classify them as originating from either a gaseous or solid source, would encourage the widespread utilization of this technique. This paper reports the application of new signal processing techniques to the analysis and classification of embolic signals. We applied a wavelet neural network algorithm to approximate the embolic signals, with the parameters of the wavelet nodes being used to train a neural network to classify these signals as resulting from normal flow, or from gaseous or solid emboli.

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A. E. Ruano

University of the Algarve

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W. C. A. Pereira

Federal University of Rio de Janeiro

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Ana Leiria

University of the Algarve

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C. A. Teixeira

University of the Algarve

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Elmira Hajimani

University of the Algarve

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M. M. M. Moura

University of the Algarve

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