An Efficient Approach for Cell Segmentation in Phase Contrast Microscopy Images
This work addresses cell segmentation for microscopy image analysis, presenting an incremental improvement in efficiency.
The paper tackles cell segmentation in phase contrast microscopy images by using a low-rank and structured sparse matrix decomposition to separate cells from the background, followed by an inverse diffraction pattern filtering method for individual cell segmentation, which reduces computational complexity compared to other restoration methods.
In this paper, we propose a new model to segment cells in phase contrast microscopy images. Cell images collected from the similar scenario share a similar background. Inspired by this, we separate cells from the background in images by formulating the problem as a low-rank and structured sparse matrix decomposition problem. Then, we propose the inverse diffraction pattern filtering method to further segment individual cells in the images. This is a deconvolution process that has a much lower computational complexity when compared to the other restoration methods. Experiments demonstrate the effectiveness of the proposed model when it is compared with recent works.