Yuan Feng

US Patent:

20180283950, Oct 4, 2018

Filed:

Mar 30, 2018

Appl. No.:

15/941510

Inventors:

- OAKLAND CA, US
Yuan Feng - Irvine CA, US
Ilya Vinogradov - Irvine CA, US

Assignee:

THE REGENTS OF THE UNIVERSITY OF CALIFORNIA - OAKLAND CA

International Classification:

G01J 3/45
G01J 3/02

Abstract:

A heterodyne optical spectroscopy system comprises a light source that acts as a local oscillator (LO); a beam splitting component that generates a reference beam from the LO; a signal component that generates a sample signal from a sample; a beam blocker that can turn off the sample signal to generate blank shots; a composite signal detection subsystem that detects a heterodyned signal that is a mix of the sample signal and a portion of the LO; a composite reference detection subsystem synchronized to the signal detection subsystem to detect a portion of the reference beam; and a processor that processes digital signals from the signal detection subsystem and the reference detection subsystem. A very versatile reference scheme is developed to treat different heterodyne spectroscopies in a unified way, which achieves optimal noise suppression.

US Patent:

20180114313, Apr 26, 2018

Filed:

Dec 22, 2016

Appl. No.:

15/387886

Inventors:

Yuan FENG - Northborough MA, US
Long HUANG - Northborough MA, US

International Classification:

G06T 7/00
G06T 7/11
G06K 9/62
G06K 9/52
G06K 9/46
A61B 5/00
A61B 6/00

Abstract:

A medical image segmentation method includes: step , initialize of the number of clusters and determine the initial values of the cluster centroids; step , calculate a Hausdorff distance between each cluster centroid and each pixel in the image; step , calculate a membership function of each pixel based on the Hausdorff distance and a Euclid distance between the cluster centroids and each pixel; step , calculate an objective function, cluster the pixels of the image based on the updated membership function, and update the centroid value; and repeating step -, until a difference between two objective function values is less than a threshold value. Then the membership function from the last iteration is a final segmentation.