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US 10,593,040 B2Granted
Methods for screening and diagnosing a skin condition
AssigneeUniversity of Houston System
InventorGeorge Zouridakis
Life of this patent
Estimating the term…
Abstract
Provided herein are digital-implemented methods for performing simultaneous analyses on an object on the skin of an animal body, for example, a human, to classify the object as a skin cancer, an ulcer or neither. The analyses are performed simultaneously on a hand-held imaging device.
Claims13
A digital processor-implemented method for performing simultaneous analyses of an object on the skin of an animal body, comprising the processor executable steps of:
- imaging the object with a hand-held imaging device;
- performing simultaneously on the image of the object an analysis of an object of interest as a skin cancer and an analysis of the object as an ulcer;
- and
- classifying the object as a skin cancer or as an ulcer or as neither from results obtained from the skin cancer analysis and the ulcer analysis,
- wherein the step of performing simultaneously the analysis of the object of interest comprises the processor executable steps of:
- segmenting the imaged object to detect a border of the object;
- and
- extracting features from the segmented object image,
- wherein the processor executable step of segmenting comprises:
- determining an initial contour of the imaged object;
- classifying pixels as contained within the initial contour as foreground, as contained without the initial contour as background or as remaining pixels if they are not classified as foreground pixels or background pixels;
- and
- applying a supervised classifier to the remaining pixels for classification as foreground or background,
- wherein the processor executable step of extracting comprises:
- dividing the segmented object image into regions based on saliency values calculated for at least one patch within the segmented object;
- dividing the regions into two regions of higher or lower intensity based on average intensity values thereof;
- and
- extracting feature representations from a sampling of patches within the intensity regions based on sampling percentages determined for the regions, and
- wherein the analysis is for an ulcer and is performed simultaneously with steps for classifying the skin cancer, said processor executable step of classifying comprising:
- inputting the extracted feature representations from the object imaged on the skin into a support vector machine trained with manually segmented objects from an ulcer;
- and
- classifying the object as an ulcer or as not an ulcer based on a comparison of the inputted extracted features with those in the support vector machine trained on a known ulcer.
19 more elements
A digital processor-implemented method for performing in real time simultaneous analyses of an object on human skin, comprising the processor executable steps of:
- imaging the object with a portable smart device;
- performing simultaneously on the image of the object an analysis of an object of interest as a skin cancer and an analysis of the object as an ulcer;
- classifying the object as a skin cancer or as an ulcer or as neither from results obtained from the skin cancer analysis and the ulcer analysis;
- and
- displaying the results as each result occurs,
- wherein the analysis is for a skin cancer, said processor executable steps comprising:
- a) obtain luminance and color components of the imaged object;
- b) classify pixels comprising the image as object pixels, if they belong to a common luminance and color foreground, as background pixels if they belong to a common luminance and color background or as remaining pixels if they are not classified as foreground pixels or background pixels;
- and
- c) apply a supervised classifier to the remaining pixels to classify them as object or background;
- d) calculate a saliency value for a plurality of patches within the segmented object and separate the patches into regions based on the saliency values;
- e) calculate an average intensity for the regions to identify them as a higher or as a lower intensity region;
- f) determine a sampling percentage for the intensity regions;
- g) sample patches within the intensity regions by corresponding sampling percentages;
- h) extract one or more feature representations for the object;
- i) training a support vector machine (SVM) with known manually segmented objects from a skin cancer;
- and
- j)classifying the object as a skin cancer or not a skin cancer based on the extracted feature representations inputted into the SVM trained with the manually segmented objects from a known skin cancer, and
- wherein the analysis is for an ulcer, said processor executable steps comprising:
- performing simultaneously steps b) to h) for classifying the skin cancer, on the image obtained in step a), and
- wherein the analysis is for an ulcer, said processor executable steps comprising:
- performing simultaneously steps b) to h) for classifying the skin cancer, on the image obtained in step a);
- i) training a support vector machine (SVM) with known manually segmented objects from an ulcer;
- and
- j) classifying the object as an ulcer or not an ulcer based on the extracted feature representations inputted into the SVM trained with the manually segmented objects from a known ulcer.
22 more elements
Dependencies are inferred from the claim text.