Sid Ahmed Soumia, Messali Zoubeida, Poyer Florent, Lumbroso-Le Rouic Livia, Desjardins Laurence, Cassoux Nathalie, Thomas Carole D, Marco Sergio, Lemaitre Stéphanie
Faculty of Science and Technology, Mohamed El Bachir El Ibrahimi University, Bordj Bou Arreridj, Algeria.
INSERM, U1196, Université Paris Sud, Université Paris-Saclay, Orsay, France.
Ophthalmic Res. 2018;59(3):164-169. doi: 10.1159/000486283. Epub 2018 Mar 26.
Due to the presence of speckle Poisson noise, the interpretation of spectral domain-optical coherence tomography (SD-OCT) images frequently requires the use of data averaging to improve the signal-to-noise ratio. This implies long acquisition times and requires patient sedation in some cases. Iterative variance stabilizing transformation (VST) is a possible approach by which to remove speckle Poisson noise on single images.
We used SD-OCT images of human and murine (LH Beta-Tag mouse model) retinas with and without retinoblastoma acquired with 2 different imaging devices (Bioptigen and Micron IV). These images were processed using a denoising workflow implemented in Matlab.
We demonstrated the presence of speckle Poisson noise, which can be removed by a VST-based approach. This approach is robust as it works in all used imaging devices and in both human and mouse retinas, independently of the tumor status. The implemented algorithm is freely available from the authors on demand.
On a single denoised image, the proposed method provides results similar to those expected from the SD-OCT averaging. Because of the friendly user interface, it can be easily used by clinicians and researchers in ophthalmology.
由于存在散斑泊松噪声，光谱域光学相干断层扫描（SD-OCT）图像的解读常常需要采用数据平均法来提高信噪比。这意味着采集时间较长，且在某些情况下需要对患者进行镇静。迭代方差稳定变换（VST）是一种可用于去除单幅图像上散斑泊松噪声的方法。
我们使用了通过2种不同成像设备（Bioptigen和Micron IV）采集的、患有和未患视网膜母细胞瘤的人类及小鼠（LH Beta-Tag小鼠模型）视网膜的SD-OCT图像。这些图像使用在Matlab中实现的去噪工作流程进行处理。
我们证实了散斑泊松噪声的存在，其可通过基于VST的方法去除。该方法具有鲁棒性，因为它在所有使用的成像设备以及人类和小鼠视网膜中均有效，且与肿瘤状态无关。所实现的算法可应作者要求免费获取。
在单幅去噪图像上，所提方法提供的结果与SD-OCT平均法预期的结果相似。由于用户界面友好，眼科临床医生和研究人员可轻松使用该方法。
