Apparatus and method for increasing the sensitivity in the detection of optical coherence tomography and low coherence interferometry (“LCI”) signals by detecting a parallel set of spectral bands, each band being a unique combination of optical frequencies. The LCI broad bandwidth source is split into N spectral bands. The N spectral bands are individually detected and processed to provide an increase in the signal-to-noise ratio by a factor of N. Each spectral band is detected by a separate photo detector and amplified. For each spectral band the signal is band pass filtered around the signal band by analog electronics and digitized, or, alternatively, the signal may be digitized and band pass filtered in software. As a consequence, the shot noise contribution to the signal is reduced by a factor equal to the number of spectral bands. The signal remains the same. The reduction of the shot noise increases the dynamic range and sensitivity of the system.

Apparatus and method for ranging and noise reduction of low coherence interferometry LCI and optical coherence tomography OCT signals by parallel detection of spectral bands

An apparatus and method are provided. In particular, at least one first electro-magnetic radiation may be provided to a sample and at least one second electro-magnetic radiation can be provided to a non-reflective reference. A frequency of the first and/or second radiations varies over time. An interference is detected between at least one third radiation associated with the first radiation and at least one fourth radiation associated with the second radiation. Alternatively, the first electro-magnetic radiation and/or second electro-magnetic radiation have a spectrum which changes over time. The spectrum may contain multiple frequencies at a particular time. In addition, it is possible to detect the interference signal between the third radiation and the fourth radiation in a first polarization state. Further, it may be preferable to detect a further interference signal between the third and fourth radiations in a second polarization state which is different from the first polarization state. The first and/or second electro-magnetic radiations may have a spectrum whose mean frequency changes substantially continuously over time at a tuning speed that is greater than 100 Tera Hertz per millisecond.

Method and apparatus for performing optical imaging using frequency-domain interferometry

A system and method for imaging of a sample, e.g., biological sample, are provided. In particular, at least one source electro-magnetic radiation forwarded to the sample and a reference may be generated. A plurality of detectors may be used, at least one of the detectors capable of detecting a signal associated with a combination of at least one first electro-magnetic radiation received from the sample and at least one second electro-magnetic radiation received from the reference. At least one particular detector may have a particular electrical integration time, and can receive at least a portion of the signal for a time duration which has a first portion with a first power level greater than a predetermined threshold and a second portion immediately preceding or following the first portion. The second portion may have a second power level that is less than the predetermined threshold, and extends for a time period which may be, e.g., approximately more than 10% of the particular electrical integration time.

System and method for optical coherence imaging

Exemplary method, apparatus and arrangement can be provided for obtaining information associated with a sample such as a portion of an anatomical structure. The information can be generated using first data, which can be based on a signal obtained from a location on the sample, and second data, where the second data can be obtained by combining a second signal received from the sample with a third reference signal. An image of a portion of the sample can also be generated based on the information. For example, the first data can be associated with spectral encoding microscopy data, and the second data can be associated with optical coherence tomography data.

Method and apparatus for optical imaging via spectral encoding

An apparatus for needle biopsy with real time tissue differentiation using one dimensional interferometric ranging imaging, comprising a biopsy device having a barrel and a needle, an optical fiber inserted in the needle, and a fiber optic imaging system connected to the optical fiber. The imaging system obtains images and compares the optical properties and patterns to a database of normalized tissue sample images to determine different tissue types. The physician performing the biopsy obtains feedback via a feedback unit associated with the biopsy device and which is connected to the imaging system. The feedback unit can provide visual, audible or vibratory feedback as to tissue type encountered when the needle is inserted toward the target tissue. The feedback unit can be programmed for different biopsy procedures so that the user can actuate a button to select a display or other feedback mechanism for the desired procedure and anticipated tissue to be encountered.

System and Method for Identifying Tissue Using Low-Coherence Interferometry

Digitized image data are input into a processor where a detection component identifies the areas (objects) of particular interest in the image and, by segmentation, separates those objects from the background. A feature extraction component formulates numerical values relevant to the classification task from the segmented objects. Results of the preceding analysis steps are input into a trained learning machine classifier which produces an output which may consist of an index discriminating between two possible diagnoses, or some other output in the desired output format. In one embodiment, digitized image data are input into a plurality of subsystems, each subsystem having one or more support vector machines. Pre-processing may include the use of known transformations which facilitate extraction of the useful data. Each subsystem analyzes the data relevant to a different feature or characteristic found within the image. Once each subsystem completes its analysis and classification, the output for all subsystems is input into an overall support vector machine analyzer which combines the data to make a diagnosis, decision or other action which utilizes the knowledge obtained from the image.

Computer-aided image analysis

Computerized image analysis

Un procedimiento implementado por ordenador para el analisis de una imagen digitalizada, comprendiendo el procedimiento: (a) ingresar un conjunto de entrenamiento de datos de imagenes y un conjunto de prueba de datos de imagenes a un procesador, para ejecutar una pluralidad de subsistemas de deteccion, estando cada subsistema de deteccion adaptado para detectar y clasificar uno entre una pluralidad de distintos rasgos de interes, en donde cada rasgo de interes tiene una pluralidad de posibles caracteristicas del rasgo, y en donde cada subsistema de deteccion genera una salida para su correspondiente rasgo de interes, en donde cada subsistema de deteccion ejecuta las etapas de: (i) preprocesar cada conjunto de datos de imagenes para detectar y extraer la presencia de un rasgo de interes dentro de los datos de imagenes; (ii) entrenar y probar al menos una maquina de vectores de soporte con al menos un nucleo que utiliza los conjuntos preprocesados de datos de imagenes para clasificar el rasgo de interes extraido en al menos una entre una pluralidad de clases de posibles caracteristicas del rasgo; (iii) comparar los rasgos clasificados a partir del conjunto de prueba de datos de imagenes con resultados conocidos del conjunto de prueba de datos de imagenes, a fin de determinar si se obtiene una solucion optima; (iv) repetir las etapas (ii) y (iii) si no se obtiene la solucion optima; (v) si se obtiene la solucion optima, ingresar un conjunto real de datos de imagenes en el procesador; (vi) preprocesar el conjunto real de datos de imagenes para detectar y extraer la presencia de rasgos de interes dentro de los datos de imagenes; (vii) clasificar al menos dicho rasgo de interes; y (viii) generar una salida que comprende el rasgo, o rasgos, de interes clasificado(s) a partir del conjunto real de datos de imagenes; (b) ingresar las salidas de cada uno de la pluralidad de subsistemas en una maquina global de vectores de soporte con al menos un nucleo; y (c) generar una unica salida global que comprende una clasificacion de la imagen digitalizada.

Analisis de imagenes asistido por erdenador.

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Computer-aided image analysis

Computerized image analysis

Analisis de imagenes asistido por erdenador.