Three-dimensional representation of skin structure

Patent metadata

Field	Value
Publication number	US-10856799-B2
Application number	US-201716090172-A
Country	US
Kind code	B2
Filing date	Mar 28, 2017
Priority date	Mar 28, 2016
Publication date	Dec 8, 2020
Grant date	Dec 8, 2020

Abstract

The present disclosure generally relates to an automated method and system for generating a three-dimensional (3D) representation of a skin structure of a subject. The method comprises: acquiring a plurality of two-dimensional (2D) cross-sectional images of the skin structure, specifically, using optical coherence tomography (OCT) technique; computing a cost for each 2D cross-sectional image based on a cost function, the cost function comprising an edge-based parameter and a non-edge-based parameter; constructing a 3D graph from the 2D cross-sectional images; and determining a minimum-cost closed set from the 3D graph based on the computed costs for the 2D cross-sectional images, wherein the 3D representation of the skin structure is generated from the minimum-cost closed set.

First claim

What is claimed is:

1. An automated method for generating a three-dimensional (3D) representation of a skin structure of a subject, the method comprising:
acquiring a plurality of two-dimensional (2D) cross-sectional images of the skin structure, each 2D cross-sectional image comprising a skin surface profile;
computing a cost for each 2D cross-sectional image based on a cost function, the cost function comprising an edge-based parameter associated with gradient information of the skin surface profile and a non-edge-based parameter associated with homogeneity information of the 2D cross-sectional image above and below the skin surface profile;
constructing a 3D graph from the 2D cross-sectional images; and
determining a minimum-cost closed set from the 3D graph based on the computed costs for the 2D cross-sectional images,
wherein the 3D representation of the skin structure comprising the skin surface profile is generated from the minimum-cost closed set.

2. The method according to claim 1 , wherein computing the costs for the 2D cross-sectional images comprises computing a cost for each pixel of each 2D cross-sectional image.

3. The method according to claim 1 , further comprising performing skin topographic analysis on the 3D representation to assess skin roughness of the subject.

4. The method according to claim 3 , wherein the skin topographic analysis comprises performing a plane rectification process.

5. The method according to claim 4 , wherein the skin topographic analysis further comprises generating a 2D depth map.

6. The method according to claim 5 , wherein the skin topographic analysis further comprises computing a set of roughness parameters.

7. The method according to claim 6 , wherein the roughness parameters are calculated based on a sliding window approach on the 2D depth map.

8. The method according to claim 6 , wherein the set of roughness parameters comprises amplitude and frequency parameters.

9. A system for generating a three-dimensional (3D) representation of a skin structure of a subject, the system comprising a processor configured for performing operations comprising:
acquiring a plurality of two-dimensional (2D) cross-sectional images of the skin structure, each 2D cross-sectional images comprising a skin surface profile;
computing a cost for each 2D cross-sectional image based on a cost function, the cost function comprising an edge-based parameter associated with gradient information of the skin surface profile and a non-edge-based parameter associated with homogeneity information of the 2D cross-sectional image above and below the skin surface profile;
constructing a 3D graph from the 2D cross-sectional images; and
determining a minimum-cost closed set from the 3D graph based on the computed costs for the 2D cross-sectional images,
wherein the 3D representation of the skin structure comprising the skin surface profile is generated from the minimum-cost closed set.

10. The system according to claim 9 , wherein computing the costs for the 2D cross-sectional images comprises computing a cost for each pixel of each 2D cross-sectional image.

11. The system according to claim 9 , wherein the non-edge-based parameter is associated with a measure of a dark to bright transition at the skin surface profile.

12. The system according to claim 9 , the operations further comprising performing a skin topographic analysis on the 3D representation to assess skin roughness of the subject.

13. The method according to claim 1 , wherein the edge-based parameter comprises an orientation penalty function based on gradient orientation.

14. The method according to claim 13 , wherein the edge-based parameter further comprises a thresholding function that suppresses pixels where a first image derivative is below a first threshold and a second image derivative is below a second threshold.

15. The method according to claim 14 , further comprising computing the first and second image derivatives using a Gaussian kernel and a Scharr operator.

16. The method according to claim 1 , wherein the non-edge-based parameter is associated with a measure of a dark to bright transition at the skin surface profile.

17. The method according to claim 16 , wherein the non-edge-based parameter is associated with a measure of a number of bright pixels above each pixel.

18. The system according to claim 9 , wherein the edge-based parameter comprises an orientation penalty function based on gradient orientation.

19. The system according to claim 18 , wherein the edge-based parameter further comprises a thresholding function that suppresses pixels where a first image derivative is below a first threshold and a second image derivative is below a second threshold.

20. The system according to claim 11 , wherein the non-edge-based parameter is associated with a measure of a number of bright pixels above each pixel.

Assignees

• Agency Science Tech & Res
• Nat Skin Centre Singapore Pte Ltd

Inventors

• Tey Hong Liang
• Srivastava Ruchir
• Yow Ai Ping
• Cheng Jun
• Li Annan
• Wong Wing Kee Damon
• Liu Jiang
• Wall Carolin

Classifications

• G06T2207/10101

  Optical tomography; Optical coherence tomography [OCT] · CPC title

• A61B2576/02

  specially adapted for a particular organ or body part · CPC title

• G06T2207/30088

  Skin; Dermal · CPC title

• G06T7/12

  Edge-based segmentation · CPC title

• G06T7/162

  involving graph-based methods · CPC title