Linear-based Eulerian motion modulation

• US 9,811,901 B2
• Filed: 03/26/2013
• Issued: 11/07/2017
• Est. Priority Date: 09/07/2012
• Status: Active Grant

• Current Assignee: Massachusetts Institute of Technology, Quanta Computer
• Original Assignee: Massachusetts Institute of Technology, Quanta Computer
• Inventors: Wu, Hao-yu, Rubinstein, Michael, Shih, Eugene Inghaw, Guttag, John V., Durand, Frederic, Freeman, William T.
• Primary Examiner(s): CONNER, SEAN M
• Application Number:
  US13/850,717
• Publication Number:
  US 20140072190A1
• Time in Patent Office: 1,687 Days
• Field of Search: None
• CPC Class Codes: G06T 2207/20016 Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
• CPC Class Codes: G06T 2207/30076 Plethysmography
• CPC Class Codes: G06T 2207/30088 Skin; Dermal
• CPC Class Codes: G06T 2207/30104 Vascular flow; Blood flow; Perfusion
• CPC Class Codes: G06T 2207/30201 Face
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/0016 involving temporal comparison
• CPC Class Codes: G06T 7/254 involving subtraction of images
• CPC Class Codes: G06T 7/262 using transform domain methods, e.g. Fourier domain methods

Abstract

In one embodiment, a method of amplifying temporal variation in at least two images comprises examining pixel values of the at least two images. The temporal variation of the pixel values between the at least two images can be below a particular threshold. The method can further include applying signal processing to the pixel values.

Claims

1. A method of amplifying temporal variation in at least two images, the method comprising:

for each particular pixel of the at least two images, detecting a temporal variation of the particular pixel of the at least two images by applying a bandpass filter, the resulting detected temporal variation of the particular pixel between the two images being below a particular threshold;

amplifying the detected temporal variation of each particular pixel; and

providing at least two modulated images by adding the amplified temporal variation to the at least two images by synthesizing the modulated images, for an amplification factor α

, according to ƒ

(x+(1+α

), δ

(t)), δ

(t) being a displacement function.

2. The method of claim 1, wherein detecting the temporal variation employs temporal processing.

3. The method of claim 1, wherein detecting the temporal variation employs spatial processing.

4. The method of claim 3, wherein the spatial processing removes noise.

5. The method of claim 1, further comprising visualizing a pattern of flow of blood in a body shown in the at least two images.

11. The method of claim 1, wherein applying the bandpass filter includes, for each pixel value, analyzing a one-dimensional (1D) image intensity.

12. The method of claim 11, wherein the 1D image intensity profile is based on a function ƒ

(x+δ

(t)), where δ

(t) is a displacement function.

6. A system for amplifying temporal variation in at least two images, the system comprising:

a pixel examination module configured to, for each particular pixel of the at least two images, detecting a temporal variation of the particular pixel of the at least two images by applying a bandpass filter, the resulting detected temporal variation between the two images being below a particular threshold; and

a signal processing module configured to;

amplify the detected temporal variation of each particular pixel, and

provide at least two modulated images by adding the amplified temporal variation to the at least two images by synthesizing the modulated images for an amplification factor α

, according to ƒ

(x+(1+α

), δ

(t)), δ

(t) being a displacement function.

7. The system of claim 6, wherein the pixel examination module is further configured to detect temporal variation by employing temporal processing.

8. The system of claim 6, wherein the signal processing is further configured to detect the temporal variation by employing spatial processing.

9. The system of claim 8, wherein the spatial processing removes noise.

10. The system of claim 6, further comprising a blood flow visualization module configured to visualize a pattern of flow of blood in a body shown in the at least two images.

13. The system of claim 6, wherein the pixel examination module is further configured to, for each pixel value, analyze a one-dimensional (1D) image intensity profile.