Sparse-representation-based denoising of photoacoustic images
Sparse-representation-based denoising of photoacoustic images
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DOI：
10.1088/2057-1976/aa7a44
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发表时间：
2017-07
影响因子：
1.4
通讯作者：
I. Haq;Ryo Nagaoka;Syahril Siregar;Y. Saijo
中科院分区：
文献类型：
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作者：
I. Haq;Ryo Nagaoka;Syahril Siregar;Y. Saijo
光学分辨率光声显微镜（OR-PAM）是一种结合光学对比度和声学分辨率的新兴混合技术。由于频率、换能器直径或激光引起的外部噪声等参数的不同，光声图像的质量会下降。换能器的直径与其近场成正比，影响换能器的聚焦或失焦，从而影响图像质量，因此光声图像的重建和去噪是医学成像中的一个重要问题，特别是在早期诊断疾病时。PA图像中不同结构的可视化需要滤波来抑制噪声。本文研究了利用k均值奇异值分解（K-means singular value decomposition, K-SVD）去除PA图像中的噪声，增强血管图像的效果。对不同直径的充血管的PA图像和利用OR-PAM成像获得的小鼠耳内图像进行了算法测试。结果表明，与标准的维纳滤波和小波滤波相比，该方法具有更好的图像去噪能力。
Optical resolution photoacoustic microscopy (OR-PAM) is an emerging hybrid technology that combines optical contrast and acoustic resolution. The quality of photoacoustic (PA) images is degraded due to different parameters such as frequency, the diameter of the transducer or external noise induced from the laser. The diameter of the transducer is proportional to its near field to focus or unfocus the transducer, which affects the image quality, so reconstruction and denoising of photoacoustic images is an important issue in medical imaging, especially when it comes to diagnosing diseases at an early stage. Visualization of different structures in the PA images requires filtering to suppress noise. This paper investigates the use of K-means singular value decomposition (K-SVD) to eliminate noise and enhance the effect of vasculature in the PA images. The algorithm is tested on PA images of blood-filled tubes of different diameters and in vivo mouse ear images acquired using OR-PAM imaging. The results reveal a better denoising capability of PA images when compared with standard Wiener and wavelet-based filtering.