The growth of pollen tubes is of significant interest in plant cell biology as it provides an understanding of internal cell dynamics that affect observable structural characteristics like cell diameter, length, and growth rate. However, these parameters can only be measured in experimental videos if the complete shape of the cell is known. The challenge is to obtain the cell boundary accurately in noisy video images. Usually these measurements are performed by a scientist who manually draws regions-ofinterest on images displayed on a computer screen. In this paper, a new automated technique is presented for boundary detection by fusing fluorescence and brightfield images, and a new efficient method of obtaining the final cell boundary through the process of Seam Carving is proposed. This approach takes advantage of the nature of the fusion process and also the shape of the pollen tube to efficiently search for the optimal cell boundary. In video segmentation, the first two frames are used to initialize the segmentation process by creating a search space based on a parametric model of the cell shape. Updates to the search space are performed based on the location of past segmentations and a prediction of the next segmentation. Experimental results show comparable accuracy to a previous method, but significant decrease in processing time. This has the potential for real time applications in pollen tube microscopy.

The growth of pollen tubes is of significant interest in plant cell biology, as it provides an understanding of internal cell dynamics that affect observable structural characteristics such as cell diameter, length, and growth rate. However, these parameters can only be measured in experimental videos if the complete shape of the cell is known. The challenge is to accurately obtain the cell boundary in noisy video images. Usually, these measurements are performed by a scientist who manually draws regions-of-interest on the images displayed on a computer screen. In this paper, a new automated technique is presented for boundary detection by fusing fluorescence and brightfield images, and a new efficient method of obtaining the final cell boundary through the process of Seam Carving is proposed. This approach takes advantage of the nature of the fusion process and also the shape of the pollen tube to efficiently search for the optimal cell boundary. In video segmentation, the first two frames are used to initialize the segmentation process by creating a search space based on a parametric model of the cell shape. Updates to the search space are performed based on the location of past segmentations and a prediction of the next segmentation.Experimental results show comparable accuracy to a previous method, but significant decrease in processing time. This has the potential for real time applications in pollen tube microscopy.

In the process of pollination, a pollen tube grows from a pollen grain that has fallen on the stigma of a flower. This tube grows towards the ovary of the flower where it will deliver male reproductive material. Knowledge of the dynamics of pollen tube growth will provide a basis for understanding more complex cells that exhibit similar growth behavior. Current pollen tube growth models are a collection of differential equations that represent the level of understanding that biologists have concerning apical growth. Due to their complex nature, these models are not used to verify observed behavior in living cells as seen under a microscope. We present a model that can be used to describe the behavior of growing pollen tube cells in actual experiments. We propose biologically relevant functions based on knowledge of the growth process to explain the dynamics of model parameters. Our model uses an affine transformation to propagate the tip of the cell and statistical parameter estimation to measure necessary parameters during growth. Using experimental videos of pollen tube growth, we show that our model can adapt to various growth scenarios while extracting growth parameters from the videos.