Method for characterization of respiratory characteristics based on voxel model

Patent metadata

Field	Value
Publication number	US-11373367-B2
Application number	US-201917056758-A
Country	US
Kind code	B2
Filing date	Nov 20, 2019
Priority date	Oct 25, 2019
Publication date	Jun 28, 2022
Grant date	Jun 28, 2022

Abstract

A method for characterization of respiratory characteristics based on a voxel model includes: successively capturing multiple frames of depth image of a thoracoabdominal surface of human body and modelling the multiple frames of depth image in 3D to obtain multiple frames of voxel model in time series; traversing voxel units of the multiple frames of voxel model and extracting a volumetric characteristic and areal characteristic of the multiple frames of voxel model; acquiring a minimum common voxel bounding box of the multiple frames of voxel model; describing spatial distribution of the multiple frames of voxel model in the form of probability and arranging the probabilities of the minimum voxel bounding boxes of individual frames of voxel model to construct a sample space of super-high dimensional vectors; reducing the dimensions of the sample space to obtain intrinsic parameters; obtaining a characteristic variable capable of characterizing the voxel model.

First claim

What is claimed is:

1. A method for characterization of respiratory characteristics based on a voxel model, comprising steps of:
successively capturing, by a camera, multiple frames of depth image of a thoracoabdominal surface of a human body and performing modelling the multiple frames of depth image in 3D to obtain multiple frames of voxel model in time series;
traversing voxel units of the multiple frames of voxel model and extracting a volumetric characteristic and an areal characteristic of the multiple frames of voxel model;
acquiring a minimum common voxel bounding box of the multiple frames of voxel model;
describing spatial distribution of the multiple frames of voxel model in the form of probability and arranging the probabilities of the minimum voxel bounding boxes of individual frames of voxel model to construct a sample space of super-high dimensional vectors;
reducing the dimensions of the sample space to obtain intrinsic parameters after dimensionality reduction; and
obtaining a characteristic variable capable of characterizing the voxel model according to the intrinsic parameters, the volumetric characteristic, and the areal characteristic,
wherein the acquiring a minimum common voxel bounding box of the multiple frames of voxel model” comprises:
representing each frame of the voxel model by M i and letting M i ∈M, M being all the frames of voxel model;
traversing M, calculating the minimum bounding box of M i , recording the length L M i , width W M i , and height H M i of the minimum bounding box, letting L Mi ∈L, W Mi ∈W, H Mi ∈H, where L is the set of the lengths of the minimum bounding boxes of all the frames of voxel model, W is the set of the widths of the minimum bounding boxes of all the frames of voxel model, and H is the set of the heights of the minimum bounding boxes of all the frames of voxel model; and
finding L max , L min , W max , W min , H max , H min in L, W, H to construct a minimum common voxel bounding box H that partitions the space according to the voxel resolution of M.

2. The method for characterization of respiratory characteristics based on a voxel model of claim 1 , wherein the “volumetric characteristic” is the overall volumetric variation state characteristic of the multiple frames of voxel model.

3. The method for characterization of respiratory characteristics based on a voxel model of claim 1 , wherein the “areal characteristic” is the overall outer-layer surface area variation state characteristic of the multiple frames of voxel model.

4. The method for characterization of respiratory characteristics based on a voxel model of claim 1 , wherein the “successively capturing, by a camera, multiple frames of depth image of a thoracoabdominal surface of a human body and modelling the multiple frames of depth image in 3D to obtain multiple frames of voxel model in time series” comprises the following steps:
S 11 successively capturing, by two stationary RGB-D cameras, multiple frames of depth image of thoracoabdominal surface of human body in motion;
S 12 denoising point cloud data in the depth image through a filter algorithm, smoothening the point cloud through a moving least square method, and merging the point cloud data through an ICP algorithm;
S 13 splitting human body data from medical platform background data in the depth image through threshold filtering;
S 14 constructing a surface model into a closed model through border interpolation;
S 15 quick fitting the three-dimensional curved surface of human body surface through Poisson's reconstruction; and
S 16 building a three-dimensional voxel model of thoracoabdominal part of human body through an Octomap to obtain multiple frames of voxel model in time series.

5. The method for characterization of respiratory characteristics based on a voxel model of claim 1 , wherein the “describing spatial distribution of the multiple frames of voxel model in the form of probability” comprises:
assuming the probability of the voxel of the voxel model occupied in the bounding box as 1 and the probability of the free voxel as 0, and obtaining spatial distribution of each frame of voxel model in the minimum common bounding box.

6. The method for characterization of respiratory characteristics based on a voxel model of claim 1 , wherein the “reducing the dimensions of the sample space” comprises reducing the dimensions of the sample space through the LLE dimensionality reduction algorithm.

7. The method for characterization of respiratory characteristics based on a voxel model of claim 1 , wherein the “obtaining a characteristic variable capable of characterizing the voxel model according to the intrinsic parameters, the volumetric characteristic, and the areal characteristic” comprises:
merging the intrinsic parameters ψ=[ψ 1 , ψ 2 , . . . , ψ m ], the volumetric characteristic V and the areal characteristic S, and obtaining a characteristic variable Γ capable of characterizing different states of the voxel model, where Γ=[VS . . . ψ 1 , ψ 2 , . . . , ψ m ] T .

Assignees

• Univ Soochow

Inventors

• Yu Shumei
• Hou Pengcheng
• Sun Rongchuan
• Kuang Shaolong
• Sun Lining

Classifications

• G06T7/55

  from multiple images · CPC title

• G06T17/00 Primary

  Three-dimensional [3D] modelling for computer graphics · CPC title

• G06T2207/10028

  Range image; Depth image; 3D point clouds · CPC title

• G06T17/10 Primary

  Constructive solid geometry [CSG] using solid primitives, e.g. cylinders, cubes · CPC title

• G06T5/20

  using local operators · CPC title