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Biomedical subjects

S Finette

Publications and source records attributed to S Finette.

6 recordsLinked to original sources
PubMed1988

Synthetic B-scan images by numerical solution of a wave equation.

We describe an efficient method for obtaining model sector-scan images by direct solution of a linear wave equation characterizing pulse scattering from spatially varying bulk modulus and density distributions. Using a pseudospectral approach, the wave equation is solved numerically for each transducer orientation to obtain a set of A-line signals. After preprocessing the raw data, sector-scan images are constructed and displayed. Examples are given for several scattering object configurations, illustrating both specular and diffractive scattering in inhomogeneous media and demonstrating the utility of the model.

Computer Simulation↗
PubMed1987

A wavefield extrapolation method for simulating B-scan images.

We describe an algorithm which improves the efficiency of simulations of B-scan images involving water path acoustical imaging systems. The method is a hybrid approach in which Rayleigh-Sommerfeld diffraction theory is used to extrapolate the propagated part of the acoustic pressure field back to the transducer while the scattered field produced by the tissue is obtained using a time-domain scattering algorithm. Considerable savings in both CPU time and array storage are achieved by elimination of discrete time updates on the computational grid between the source and the tissue.

Algorithms↗
PubMed1983

Breast tissue classification using diagnostic ultrasound and pattern recognition techniques: I. Methods of pattern recognition.

This paper discusses the application of statistical pattern recognition techniques to problems in diagnostic ultrasound. Using our own system as an example, we describe the concepts and specific methods that we have applied to a problem involving the computer-aided classification of breast tissue in vivo. Topics include feature generation, feature selection and classification, as well as a method which estimates the probability of error on classifying future data. An accompanying paper applies these methods to the classification of backscattered RF signals from normal and diseased breast tissue.

Breast Diseases↗
PubMed1983

Breast tissue classification using diagnostic ultrasound and pattern recognition techniques: II. Experimental results.

The methods of statistical pattern recognition have been applied to the problem of in vivo ultrasonic characterization of breast disease in humans. Backscattered A-mode signals obtained from a commercial pulse imaging system were used to generate a large set of potentially useful features. Using statistical tests, a small subset of discriminatory features was selected to design a Bayes decision rule for each of two tissue classification schemes: malignant disease vs. benign disease, and malignant disease vs. (benign disease + normal tissue). Classification results obtained by the rotation method included sensitivities of 88 percent and 76 percent for the two schemes, based on data obtained from 32 women. These results are encouraging, though a definitive statement concerning the extrapolation of these numbers to the general population should only be made after obtaining results with a large data base.

Breast Diseases↗
PubMed1978

Anistropic connectivity and cooperative phenomena as a basis for orientation sensitivity in the visual cortex.

A computer simulation model of the neural circuity underlying orientation sensitivity in cortical neurons is examined. The model consists of a network of 3000 neurons divided into two functionally distinct cell types: excitatory (E-cells) and inhibitory (I-cells). We demonstrate that both orientation sensitivity and shape selectivity can be accounted for by making the following assumptions: 1) thalamic afferents to a sheet of cortical neurons are retinotopically organized; 2) thalamic afferents come from a single neuron, or at most a few neurons, in the lateral geniculate nucleus; 3) cortical activity is cooperative, i.e. largely dependent on intracortical connections, some of which have anisotropies along directions parallel to the pial surface. Anisotropies are specified only by the distribution of cells which are postsynaptic to a particular neuron, without specifying the axonal or dendritic contributions. In this paper, orientation sensitivity arises through cooperative interactions among neurons having anisotropic excitatory, and isotropic inhibitory connections.

Action Potentials↗