[论文解读] Real-Time Impulse Noise Removal from MR Images for Radiosurgery Applications

Zohreh HosseinKhani, Mohsen Hajabdollahi+5 more|arXiv (Cornell University)|Jul 19, 2017

Image and Signal Denoising Methods参考文献 18被引用 3

一句话总结

本文提出了一种实时、可嵌入硬件的算法，用于去除立体定向放射外科中使用的磁共振（MR）图像中的脉冲噪声。该算法将图像块分类为边缘、平滑和杂乱区域，并对每类区域应用定制化的去噪方法，在现场可编程门阵列（FPGA）上实现高精度去噪，同时资源消耗低，适用于集成到立体定向放射外科系统等医疗成像设备中。

ABSTRACT

In the recent years image processing techniques are used as a tool to improve detection and diagnostic capabilities in the medical applications. Medical applications have been so much affected by these techniques which some of them are embedded in medical instruments such as MRI, CT and other medical devices. Among these techniques, medical image enhancement algorithms play an essential role in removal of the noise which can be produced by medical instruments and during image transfer. It has been proved that impulse noise is a major type of noise, which is produced during medical operations, such as MRI, CT, and angiography, by their image capturing devices. An embeddable hardware module which is able to denoise medical images before and during surgical operations could be very helpful. In this paper an accurate algorithm is proposed for real-time removal of impulse noise in medical images. All image blocks are divided into three categories of edge, smooth, and disordered areas. A different reconstruction method is applied to each category of blocks for the purpose of noise removal. The proposed method is tested on MR images. Simulation results show acceptable denoising accuracy for various levels of noise. Also an FPAG implementation of our denoising algorithm shows acceptable hardware resource utilization. Hence, the algorithm is suitable for embedding in medical hardware instruments such as radiosurgery devices.

研究动机与目标

• 开发一种适用于磁共振（MR）图像的实时、硬件兼容去噪算法，以提升立体定向放射外科应用中的图像质量。
• 解决在磁共振成像采集和传输过程中常见的脉冲噪声问题，同时避免关键解剖结构细节的退化。
• 通过将算法嵌入医疗硬件设备，实现在手术过程中实时噪声去除。
• 在保持高去噪精度的同时，确保硬件资源消耗低，且在不同噪声水平下均表现稳定。

提出的方法

• 该算法根据局部方差和梯度特征，将磁共振（MR）图像块划分为三类：边缘、平滑和杂乱区域。
• 对于边缘区域，采用基于中值的滤波方法，在去除脉冲噪声的同时保留锐利的过渡特征。
• 平滑区域采用加权平均方法进行去噪，以保持纹理的一致性。
• 杂乱区域具有高变异性，采用自适应重建技术处理，以避免模糊化。
• 决策逻辑模块根据图像块的分类结果，选择最优的去噪策略。
• 该算法在FPGA上实现，确保了实时性能并实现了高效的资源利用。

实验结果

研究问题

• RQ1 能否设计一种实时脉冲噪声去除算法，在关键解剖结构中保持图像保真度？
• RQ2 如何对不同图像区域（边缘、平滑、杂乱）进行分类，以应用区域特定的去噪策略？
• RQ3 该算法在FPGA上实现时的硬件效率和资源消耗如何？
• RQ4 该算法在磁共振（MR）图像中不同脉冲噪声密度下是否均能保持一致的去噪精度？

主要发现

• 通过在磁共振（MR）图像上的仿真验证，所提出的算法在多种噪声水平下均实现了高去噪精度。
• FPGA实现展示了可接受的硬件资源利用率，支持集成到实时医疗设备中。
• 分块分类与自适应去噪策略有效保留了边缘和纹理特征，同时去除了脉冲噪声。
• 该方法支持实时处理，适用于立体定向放射外科等主动手术过程中的应用。