VRVis Center for Virtual Reality and Visualization, Vienna, Austria.
Research Institute of Molecular Pathology, Vienna, Austria.
Neuroinformatics. 2016 Apr;14(2):221-33. doi: 10.1007/s12021-015-9289-y.
GAL4 gene expression imaging using confocal microscopy is a common and powerful technique used to study the nervous system of a model organism such as Drosophila melanogaster. Recent research projects focused on high throughput screenings of thousands of different driver lines, resulting in large image databases. The amount of data generated makes manual assessment tedious or even impossible. The first and most important step in any automatic image processing and data extraction pipeline is to enhance areas with relevant signal. However, data acquired via high throughput imaging tends to be less then ideal for this task, often showing high amounts of background signal. Furthermore, neuronal structures and in particular thin and elongated projections with a weak staining signal are easily lost. In this paper we present a method for enhancing the relevant signal by utilizing a Hessian-based filter to augment thin and weak tube-like structures in the image. To get optimal results, we present a novel adaptive background-aware enhancement filter parametrized with the local background intensity, which is estimated based on a common background model. We also integrate recent research on adaptive image enhancement into our approach, allowing us to propose an effective solution for known problems present in confocal microscopy images. We provide an evaluation based on annotated image data and compare our results against current state-of-the-art algorithms. The results show that our algorithm clearly outperforms the existing solutions.
利用共聚焦显微镜进行 GAL4 基因表达成像，是研究模式生物如黑腹果蝇神经系统的一种常用且强大的技术。最近的研究项目集中在对数千种不同驱动线的高通量筛选上，从而产生了大量的图像数据库。生成的数据量使得手动评估变得繁琐甚至不可能。任何自动图像处理和数据提取管道的第一步和最重要的步骤，都是增强具有相关信号的区域。然而，通过高通量成像获得的数据通常不太适合此任务，往往会显示出大量的背景信号。此外，神经元结构，特别是具有微弱染色信号的薄而细长的突起，很容易丢失。在本文中，我们提出了一种通过利用基于 Hessian 的滤波器来增强图像中细而弱的管状结构来增强相关信号的方法。为了获得最佳效果，我们提出了一种新的自适应背景感知增强滤波器，该滤波器的参数是基于常见的背景模型来估计的局部背景强度。我们还将自适应图像增强的最新研究成果纳入我们的方法中，从而能够针对共聚焦显微镜图像中存在的已知问题提出有效的解决方案。我们基于注释图像数据进行评估，并将我们的结果与当前最先进的算法进行比较。结果表明，我们的算法明显优于现有解决方案。
