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US 12,008,758 B2Granted
Detection of polyps
AssigneePsip LLC
InventorsSalmaan Hameed, Giau Nguyen
Life of this patent
Estimating the term…
Abstract
Identifying polyps or lesions in a colon. In some variations, computer-implemented methods for polyp detection may be used in conjunction with an endoscope system to analyze the images captured by the endoscopic system, identify any polyps and/or lesions in a visual scene captured by the endoscopic system, and provide an indication to the practitioner that a polyp and/or lesion has been detected.
Claims32
A method for lesion detection, comprising the steps of:
- in an image of a body region acquired from an imaging module located at a distal portion of an endoscope, by processor of a computer, identifying surface peaks in the acquired image, the surface peaks identified via histograms of oriented gradients (HOG) and/or maximally stable extremal region (MSER);
- by processor of the computer, identifying regions of interest around the one or more identified surface peaks based on one or more of clustering of surface peaks, a separation distance, surface peak density, high intensity pixel densities, size and shape of high-contrast edges, spatial frequency of low-contrast edges, RGB values, and/or changes of RGB values across a region;
- by processor of the computer, within the region(s) of interest, identifying features likely to be lesions of interest by applying a convolutional neural network (CNN) designed to compare the region(s) of interest to with a plurality of images containing lesions and a plurality of images that do not contain lesions;
- and
- for features likely to be lesions of interest based on application of the CNN, computing an indication of the identification of the identified likelihood.
2 more elements
An apparatus, comprising:
- a processor, and a non-transitory memory having stored therein one or more programs programmed to cause the processor to:
- in an image of a body region acquired from an imaging module located at a distal portion of an endoscope, identify surface peaks in the image, the surface peaks identified via histograms of oriented gradients (HOG) and/or maximally stable extremal region (MSER);
- identify regions of interest around the one or more identified surface peaks based on one or more of clustering of surface peaks, a separation distance, surface peak density, high intensity pixel densities, size and shape of high-contrast edges, spatial frequency of low-contrast edges, RGB values, and/or changes of RGB values across a region;
- within the region(s) of interest, identify features likely to be lesions of interest by applying a convolutional neural network (CNN) designed to compare the region(s) of interest to with a plurality of images containing lesions and a plurality of images that do not contain lesions;
- and
- for features likely to be lesions of interest based on application of the CNN, compute an indication of the identification of the identified likelihood.
3 more elements
Dependencies are inferred from the claim text.