Prasath V B S, Pelapur R, Glinskii O V, Glinsky V V, Huxley V H, Palaniappan K
Department of Computer Science, University of Missouri-Columbia, Columbia, MO 65211 USA.
Department of Medical Pharmacology and Physiology, University of Missouri-Columbia, Columbia, MO 65211 USA ; National Center for Gender Physiology, University of Missouri-Columbia, Columbia, MO 65211 USA ; Research Service, Harry S. Truman Memorial Veterans Hospital, Columbia, MO 65201 USA.
Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:540-543. doi: 10.1109/ISBI.2015.7163930.
Fluorescence microscopy images are contaminated by noise and improving image quality without blurring vascular structures by filtering is an important step in automatic image analysis. The application of interest here is to automatically extract the structural components of the microvascular system with accuracy from images acquired by fluorescence microscopy. A robust denoising process is necessary in order to extract accurate vascular morphology information. For this purpose, we propose a multiscale tensor with anisotropic diffusion model which progressively and adaptively updates the amount of smoothing while preserving vessel boundaries accurately. Based on a coherency enhancing flow with planar confidence measure and fused 3D structure information, our method integrates multiple scales for microvasculature preservation and noise removal membrane structures. Experimental results on simulated synthetic images and epifluorescence images show the advantage of our improvement over other related diffusion filters. We further show that the proposed multiscale integration approach improves denoising accuracy of different tensor diffusion methods to obtain better microvasculature segmentation.
荧光显微镜图像会受到噪声污染，在不通过滤波模糊血管结构的情况下提高图像质量是自动图像分析中的重要一步。这里感兴趣的应用是从荧光显微镜获取的图像中准确自动提取微血管系统的结构成分。为了提取准确的血管形态信息，需要一个强大的去噪过程。为此，我们提出了一种具有各向异性扩散模型的多尺度张量，它在准确保留血管边界的同时逐步自适应地更新平滑量。基于具有平面置信度度量的相干增强流和融合的三维结构信息，我们的方法集成了多个尺度以保留微血管并去除膜结构噪声。在模拟合成图像和落射荧光图像上的实验结果表明了我们的改进相对于其他相关扩散滤波器的优势。我们进一步表明，所提出的多尺度集成方法提高了不同张量扩散方法的去噪精度，以获得更好的微血管分割效果。
