A BLIND MONTE CARLO DETECTION-ESTIMATION METHOD FOR OPTICAL COHERENCE TOMOGRAPHY

被引：7

作者：

Kail, Georg ^{[1
]}

Novak, Clemens ^{[1
]}

Hofer, Bernd ^{[2
]}

Hlawatsch, Franz ^{[1
]}

机构：

[1] Vienna Univ Technol, Inst Commun & Radio Frequency Engn, Gusshausstr 25-389, A-1040 Vienna, Austria

[2] Cardiff Univ, Sch Optometry & Vis Sci, Cardiff CF24 4LU, Wales

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS | 2009年

关键词：

Optical coherence tomography; Bayesian analysis; Gibbs sampler; Monte Carlo method; detection; estimation; Bernoulli-Gaussian model; blind deconvolution;

D O I：

10.1109/ICASSP.2009.4959628

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

We consider the parametric analysis of frequency-domain optical coherence tomography (OCT) signals. A Monte Carlo (Gibbs sampler) detection-estimation method for determining the depths and reflection coefficients of tissue interfaces (reflective sites in the tissue) is proposed. Our method is blind since it estimates the instrumentation-dependent "fringe" function along with the tissue parameters. Sparsity of the detected interfaces is enforced by an impulse detector and a modified Bernoulli-Gaussian prior with a minimum distance constraint. Numerical results using synthetic and real signals demonstrate the excellent performance and fast convergence of our method.

引用

页码：493 / +

页数：2

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