No abstract

The automated estimation of cell division rate plays an important role in the evaluation of a gene function in high throughput biomedical research. Using Computerized Video Time-Lapse (CVTL) microcopy , it is possible to follow a large number of cells in their physiological conditions for several generations. However analysis of this large volume data is complicated due to cell to cell contacts in a high density population. We approach this problem by segmenting out cells or cell clusters through a learning method. The feature of a pixel is represented by the intensity and gradient information in a small surrounding sub-window. Curve evolution techniques are used to accurately find the cell or cell cluster boundary. With the assumption that the average cell size is the same in each frame, we can use the cell area to estimate the cell division rate. Our segmentation results are compared to manually-defined ground truth. Both recall and precision measures for segmentation accuracy are above 95%.

Quercus aquifolioides is one of the most representative broad-leaved plants in Qinghai-Tibet Plateau with important ecological status. So far, understanding how to quickly estimate the chlorophyll content of plants in plateau areas is still an urgent problem. Field Spec 3 spectrometer was used to measure hyperspectral reflectance data of Quercus aquifolioides leaves at different altitudes, and CCI (chlorophyll relative content) of corresponding leaves was measured by a chlorophyll meter. The correlation and univariate linear fitting analysis techniques were used to establish their relationship models. The results showed that: (1) Chlorophyll relative content of Quercus aquifolioides, under different altitude gradients, were significantly different. From 2905 m to 3500 m, chlorophyll relative content increased first and then decreased. Altitude 3300 m was the most suitable growth area. (2) In 350~550 nm, the spectral reflectance was 3500 m > 3300 m > 2905 m. In 750~1100 nm, the spectral reflectivity was 2905 m > 3500 m > 3300 m. (3) There were 4 main reflection peaks and 5 main absorption valleys in the leaf surface spectral reflection curve. While, 750~1400 nm was the sensitive range of leaf spectral response of Quercus aquifolioides. (4) The red edge position and red valley position moved to short wave direction with the increase of altitude, while the yellow edge position and green peak position moved to long wave direction first and then to short wave direction. (5) The correlation curve between the original spectrum and the CCI value was the best between the wavelengths 509~650 nm. The correlation between the first derivative spectrum and CCI value was the best and most stable at 450~500 nm. The green peak reflectance was most sensitive to the relative chlorophyll content of Quercus aquifolioides. The estimation model R2 of green peak reflectance was the highest (y = 206.98e−10.85x, R2 = 0.8523), and the prediction accuracy was 95.85%. The research results can provide some technical and theoretical support for the protection of natural Quercus aquifolioides forests in Tibet.

Rapidly determining leaf vein network patterns and vein densities is biologically important and technically challenging. Current methods, however, are limited to vein contour extraction. Further image processing is difficult, and some leaf vein traits of interest therefore cannot be quantified. In this study, we proposed a novel method for the fast and accurate determination of leaf vein network patterns and vein density. Nine tree species with different leaf characteristics and vein types were applied to verify this method. To overcome the image processing difficulties at the microscopic scale, we adopted the remote object-oriented classification method applied comprehensively in the field of remote sensing research. The key to this approach is to determine the universally applicable leaf vein extraction threshold values (scale parameter, shape parameter, compactness parameter, brightness feature, spectral feature and geometric feature). Based on our analysis, the following recommended threshold values were determined: the scale parameter was 250, the shape parameter was 0.7, the compactness parameter was 0.3, the brightness feature value was 230∼280, the spectral feature value was 180∼230, and the geometric feature value was less than 2. With the optimal extraction parameters applied, the extraction precision was above 96.40% on average for the nine species studied. The leaf vein density calculation rate increased by more than 87.3% compared to that of the traditional methods. The results showed that this method is accurate, fast, flexible and complementary to existing technologies. It is an effective tool for the fast extraction of vein networks and the exploration of leaf vein characteristics, particularly for large-scale studies in plant vein physiology.