Automated surface area assessment for dermatologic lesions
AssigneeMassachusetts Institute of Technology
InventorsAnantha P. Chandrakasan, Priyanka Raina, Jiarui Huang, Victor Huang
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Abstract
A method for assessing a three-dimensional (3D) surface area having one or more lesions is disclosed. The method includes steps of: capturing a two-dimensional (2D) color image and a depth image of the 3D surface area; enhancing contrast of the 2D color image; segmenting the one or more lesions of the 2D color image into one or more segmented lesions; and calculating 3D area of the one or more segmented lesions using information from 2D color image and the depth image.
Claims19
A method for assessing a three-dimensional (3D) surface area having one or more lesions, the method comprising:
- capturing a two-dimensional (2D) color image and a depth image of the 3D surface area, the depth image being an image in which each pixel has associated therewith a distance to a corresponding point on the surface being imaged;
- enhancing contrast of the 2D color image;
- segmenting the one or more lesions of the 2D color image into one or more segmented lesions;
- and
- calculating a 3D surface area of the one or more segmented lesions using enhanced contrast information from the 2D color image and distance information from the depth image.
An apparatus for assessing a three-dimensional (3D) surface area having one or more lesions, the apparatus comprising:
- a camera capturing a two-dimensional (2D) color image and a depth image of the 3D surface area, the depth image being an image in which each pixel has associated therewith a distance to a corresponding point on the surface being imaged;
- and
- a processor and a memory, wherein the processor is configured to:
- enhance contrast of the 2D color image;
- segment the one or more lesions of the 2D color image into one or more segmented lesions;
- and
- calculate a 3D surface area of the one or more segmented lesions using enhanced contrast information from the 2D color image and distance information from the depth image.
Dependencies are inferred from the claim text.