Multi-channel detection is an effective way to improve data throughput of spectral-domain optical coherence tomography (SDOCT). However, current multi-channel OCT requires multiple detectors, which increases the complexity and cost of the system. We propose a novel multi-channel detection design based on a single spectrometer. Each camera pixel receives interferometric spectral signals from all the channels but with a spectral shift between two channels. This design effectively broadens the spectral bandwidth of each pixel, which reduces relative intensity noise (RIN) by √M times with M being the number of channels. We theoretically analyzed the noise of the proposed design under two cases: shot-noise limited and electrical noise or RIN limited. We show both theoretically and experimentally that this design can effectively improve the sensitivity, especially for electrical noise or RIN-dominated systems.

在眼科疾病检测中，为了对被检测者进行快速、准确、自动化的瞳孔定位，提出一种改进径向对称变换的瞳孔中心点定位算法。首先利用灰度积分投影法结合最大类间方差法，完成对人眼图像的粗分割，并根据多团块筛选条件提取出只包含瞳孔的感兴趣区域(Region Of Interest，ROI)。然后对ROI采用最小外接矩形结合灰度级形态学线性滤波方法，完成搜索半径范围的设置。最后，利用改进的径向对称变换算法进行瞳孔中心点定位。实验结果表明：本文算法的定位误差在8 pixel以内，平均定位时间为0.366 s，能够适应人眼图像中噪声干扰、采集不完整等大量非理性状态，满足多种红外眼科疾病检测设备对瞳孔定位算法的要求。

多帧叠加平均处理是去除扫频光学相干层析系统散斑噪声、获得较为清晰结构信息的有效方法，但眼睛的震颤、漂移、微眼跳等生理特性和系统光路特性会使图像之间存在错位，导致叠加效果不佳、结构稳定性差，为此本文提出一种基于灰度分布信息和目标几何信息相结合的配准算法。该方法根据图像平均灰度分布提取包含目标信息的感兴趣区域，通过相位相关算法和基于分段拟合的灰度投影算法的双重作用校正图像的平移变换；通过拟合视网膜上边界作为特征点迭代确定最佳旋转参数，并再次重新估计平移参数，实现图像的刚性配准；最后通过轴向扫描一对一映射法以能量函数为约束条件实现图像的非刚性配准。对活体兔眼进行实验，结果表明，本文算法配准后的叠加图像边界清晰，结构信息增强，信噪比和对比度平均有效提高一倍多。本算法适用于强噪声视网膜B-Scans图像的配准，能满足多种类型OCT系统的叠加成像需要，具有较高的鲁棒性和图像配准精度。