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

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%).

Cost/benefit criterion for selection of pulsed Doppler ultrasound spectral mean frequency and bandwidth estimators.

A flexible selection criterion for spectral estimators based on the weighted statistical accuracy (benefit) of estimation of decisive spectral parameters under the constraint of low computational complexity (cost) is proposed. This new cost/benefit criterion also selects the model order for parametric spectral estimators-selecting model orders significantly lower than those determined by accepted criteria. The importance of different Doppler signal parameters (e.g., mean frequency and spectral bandwidth) and their accuracy of estimation is incorporated by the use of weighting factors. The use of this method with simulated Doppler signals led to the selection of the modified covariance Alt estimator.

Nonstationarity broadening reduction in pulsed Doppler spectrum measurements using time-frequency estimators.

The spectral width of Doppler signals is used as measure of lesion-induced flow disturbance. Its estimation accuracy is compromised using the conventional short-term Fourier transform (STFT) since this method implicitly assumes signal stationarity during the signal window while the Doppler signals from arteries are markedly nonstationary. The Wigner-Ville (WVD), Choi-Williams (CWD) and Bessel distributions (BD), specifically designed for nonstationary signals, have been optimized for spectral width estimation accuracy and compared to the STFT under different signal to noise ratios using simulated Doppler signals of known time-frequency characteristics. The optimum parameter values for each method were determined as a Hanning window duration of 10 ms for the STFT, 40 ms for the WVD and CWD and 20 ms for the BD and dimensionless time-frequency smoothing constant values of five in the CWD and two in the BD. Thresholding was used to reduce the effect of cross terms and side lobes in the WVD and BD. With no added noise the WVD gave the lowest estimation error followed by the CWD. At signal-to-noise ratios (SNR's) of 10 dB and 20 dB the CWD and BD had similar errors and were markedly better than the other estimators. Overall the CWD gave the best performance.

Ultrassom de terapia / Ultrasound therapy

Temperature modeling of human tissue subjected to ultrasound for therapeutic use is essential for an accurate instrumental assessment and calibration. Prior studies with a homogeneous medium are hereby reported. Nonlinear punctual temperature modeling is proposed by means of Radial Basis Functions Neural Network (RBFNN) structures...