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
收藏
页码：493 / +
页数：2
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