可视化
光学
生物医学中的光声成像
体内
衰减系数
材料科学
临床前影像学
滤波器(信号处理)
生物医学工程
计算机科学
计算机视觉
人工智能
物理
医学
生物
生物技术
作者
Mohammadreza Amjadian,Seyed Masood Mostafavi,Jiangbo Chen,Jingyi Zhu,Jun Ma,Zhengtang Luo,Lidai Wang
出处
期刊:Optics Express
[Optica Publishing Group]
日期:2024-01-29
卷期号:32 (15): 25533-25533
摘要
Filter-based vessel enhancement algorithms facilitate the extraction of vascular networks from medical images. Traditional filter-based algorithms struggle with high noise levels in images with false vessel extraction, and a low standard deviation (σ) value may introduce gaps at the centers of wide vessels. In this paper, a robust technique with less sensitivity to parameter tuning and better noise suppression than other filter-based methods for two-dimensional and three-dimensional images is implemented. In this study, we propose a filter that employs non-local means (NLM) for denoising, applying the vesselness function to suppress blob-like structures and filling the gaps in wide vessels without compromising edge quality or details. Acoustic resolution photoacoustic microscopy (AR-PAM) systems generate high-resolution volumetric photoacoustic images, but their vascular structure imaging suffers from out-of-focal signal-to-noise ratio (SNR) and lateral resolution loss. Implementing a synthetic aperture focusing technique (SAFT) based on a virtual detector (VD) improves out-of-focal region resolution and SNR. Combining the proposed filter with the SAFT algorithm enhances vascular structural imaging in AR-PAM systems. The proposed method is robust and applicable for animal tissues with less error of vasculature structure extraction in comparison to traditional fliter-based methods like Frangi and Sato filter. Also, the method is faster in terms of processing speed and less tuning parameters. We applied the method to a digital phantom to validate our approach and conducted in vivo experiments to demonstrate its superiority for real volumetric tissue imaging.
科研通智能强力驱动
Strongly Powered by AbleSci AI