Physical Optics Corporation

Intelligent Optical Systems, Inc.

Taiwei Lu

Robert A Lieberman

Andrew A Kostrzewski

Lev S Sadovnik

Jeremy M Lerner

Zonh-Zen Ho

Allan Wang

Shing-Hong F Lin

Hung Chou

Emile Fiesler

Systems and methods using a spectrometer system for real-time automatic evaluation of tissue injury are described. A method of assessing an injury to tissue comprises reflecting an electromagnetic signal from the tissue to produce a reflected electromagnetic signal; producing spectral data pertaining to the intensities of individual wavelengths of the reflected electromagnetic signal; analyzing the spectral data to obtain a set of results; and providing an indication of the nature of the injury to the tissue based upon the set of results.

Accurate tissue injury assessment

Systems and methods using a neural network based portable absorption spectrometer system for real-time automatic evaluation of tissue injury are described. An apparatus includes an electromagnetic signal generator; an optical fiber connected to the electromagnetic signal generator; a fiber optic probe connected to the optical fiber; a broad band spectrometer connected to the fiber optic probe; and a hybrid neural network connected to the broad band spectrometer. The hybrid neural network includes a principle component analyzer of broad band spectral data obtained from said broad band spectrometer.

Accurate tissue injury assessment using hybrid neural network analysis

An apparatus for in-depth three dimensional tumor mapping including (A) a light source; (B) a multi-fiber bundle including at least one illumination fiber and at least two receiving fibers, the at least one illumination fiber being connected to the light source; (C) a spectrometer connected to the at least two receiving fibers; and (D) a hybrid neural network connected to the spectrometer, said hybrid neural network including a principle component analysis processor and a neural network classifier.

Hybrid neural network and multiple fiber probe for in-depth 3-D mapping

A method of operating an image recognition system including providing a neural network including a plurality of input neurons, a plurality of output neurons and an interconnection weight matrix; providing a display including an indicator; initializing the indicator to an initialized state; obtaining an image of a structure; digitizing the image so as to obtain a plurality of input intensity cells and define an input object space; transforming the input object space to a feature vector including a set of n scale-, position- and rotation- invariant feature signals, where n is a positive integer not greater than the plurality of input neurons, by extracting the set of n scale-, position- and rotation-invariant feature signals from the input object space according to a set of relationships I.sub.k =.intg..sub..OMEGA. .intg.I(x,y)h[k,I(x,y)]dxdy, where I.sub.k is the set of n scale-, position- and rotation-invariant feature signals, k is a series of counting numbers from 1 to n inclusive, (x,y) are the coordinates of a given cell of the plurality of input intensity cells, I(x,y) is a function of an intensity of the given cell of the plurality of input intensity cells, .OMEGA. is an area of integration of input intensity cells, and h[k,I(x,y)] is a data dependent kernel transform from a set of orthogonal functions, of I(x,y) and k; transmitting the set of n scale-, position- and rotation- invariant feature signals to the plurality of input neurons; transforming the set of n scale-, position- and rotation- invariant feature signals at the plurality of input neurons to a set of structure recognition output signals at the plurality of output neurons according to a set of relationships defined at least in part by the interconnection weight matrix of the neural network; transforming the set of structure recognition output signals to a structure classification signal; and transmitting the structure classification signal to the display so as to perceptively alter the initialized state of the indicator and display the structure recognition signal for the structure.

Method and apparatus for image recognition using invariant feature

Apparatus for analyzing a spectral signature, including: a light source; a spatial light modulator connected to the light source, the spatial light modulator modulating light from the light source in accordance with spatial features of the spectral signature; an optic system upon which modulated light from the spatial light modulator is incident, the optic system filtering the modulated light; a hologram illuminated with filtered, modulated light from the optic system, the hologram outputting an optical identification of the spectral signature; and a detector upon which the optical identification is incident, the detector detecting the optical identification.

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Torrance, CA, United States of America

Taiwei Lu