Sti Medical Systems, Inc.

Other

Holger Lange

Ulf Peter Gustafsson

Wenjing Li

Rolf Holger Wolters

Marcelo Esteban Soto-Thompson

A method of automated image calibration that corrects for non-uniform illumination and calibrates color that is simple, fast, automated, accurate and reliable. A gray balance algorithm is applied to correct for non-uniform illumination and a color calibration algorithm is then applied to calibrate the human subject data. The system has been applied in multiple clinical sites with different instruments.

Method of automated image color calibration

Uterine cervical cancer Computer-Aided-Diagnosis (CAD) according to this invention consists of a core processing system that automatically analyses data acquired from the uterine cervix and provides tissue and patient diagnosis, as well as adequacy of the examination. The data can include, but is not limited to, color still images or video, reflectance and fluorescence multi-spectral or hyper-spectral imagery, coherent optical tomography imagery, and impedance measurements, taken with and without the use of contrast agents like 3-5% acetic acid, Lugol's iodine, or 5-aminolevulinic acid. The core processing system is based on an open, modular, and feature-based architecture, designed for multi-data, multi-sensor, and multi-feature fusion. The core processing system can be embedded in different CAD system realizations. For example: A CAD system for cervical cancer screening could in a very simple version consist of a hand-held device that only acquires one digital RGB image of the uterine cervix after application of 3-5% acetic acid and provides automatically a patient diagnosis. A CAD system used as a colposcopy adjunct could provide all functions that are related to colposcopy and that can be provided by a computer, from automation of the clinical workflow to automated patient diagnosis and treatment recommendation.

Uterine cervical cancer computer-aided-diagnosis (CAD)

The image registration systems and methods according to this invention register reflectance and fluorescence hyperspectral imagery and provide the spatial image transformation between the reflectance and fluorescence hyperspectral imagery. A reflectance image is embedded into the fluorescence hyperspectral imagery and thereby resolves the resemblance problem of reflectance and fluorescence hyperspectral imagery. The reflectance image is embedded into the fluorescence hyperspectral imagery using (1) light from a narrow-band source embedded in Ultraviolet (UV) excitation light, (2) second order diffracted light, and/or (3) Ultraviolet (UV) excitation light reflectance. Resembling reflectance images are calculated from the reflectance hyperspectral imagery and the embedded reflectance image in the fluorescence hyperspectral imagery. The two respective reflectance images are pre-processed to maximize the resemblance between them. Image registration algorithms use the two pre-processed reflectance images and calculate the spatial image transformation that allows to map the spatial coordinates from the reflectance to the fluorescence hyperspectral imagery (and vice versa).

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San Diego, CA, United States of America

Holger Lange