Department of Physics, Washington University, St. Louis, Missouri, USA.
J Microsc. 2014 Jan;253(1):54-64. doi: 10.1111/jmi.12097. Epub 2013 Nov 19.
Inspired by a multiresolution community detection based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Furthermore, using the proposed method, the mean-square error in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The multiresolution community detection method appeared to perform better than a popular spectral clustering-based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in mean-square error with increasing resolution.
受一种基于多分辨率社区发现的网络分割方法的启发，我们在对两张选定图像的首次初步研究中，提出了一种自动分割荧光寿命（FLT）成像显微镜（FLIM）图像的方法。图像处理问题被定义为在 FLIM 图像中识别具有相应平均 FLT 的与背景相对的片段。所提出的方法针对使用图像像素作为节点以及像素的 FLT 之间的相似性作为边缘定义的网络的给定分辨率分割 FLIM 图像。在得到的分割中，网络的低分辨率导致较大的片段，而网络的高分辨率导致较小的片段。此外，使用所提出的方法，在 FLIM 图像中估计 FLT 片段的均方误差被发现随着相应网络分辨率的增加而一致减小。多分辨率社区发现方法在执行 FLIM 图像分割方面似乎比流行的基于谱聚类的方法表现更好。在高分辨率下，谱分割方法在其输出中引入了嘈杂的片段，并且无法随着分辨率的增加而实现均方误差的一致减小。
