Analyzing orientation patterns of human skin fibroblasts using image gradient information.

Fibroblasts are the cells within the dermis layer of skin which are responsible for generating connective tissue, where collagen is the main component. However, the effects of collagen on the motion behavior of human skin fibroblasts were not studied well. We therefore propose an image analysis method to quantify the effects of collagen on the motion characteristics of fibroblasts.

Improved and robust detection of cell nuclei from four dimensional fluorescence images.

Segmentation-free direct methods are quite efficient for automated nuclei extraction from high dimensional images. A few such methods do exist but most of them do not ensure algorithmic robustness to parameter and noise variations. In this research, we propose a method based on multiscale adaptive filtering for efficient and robust detection of nuclei centroids from four dimensional (4D) fluorescence images.

Automatic extraction of nuclei centroids of mouse embryonic cells from fluorescence microscopy images.

Accurate identification of cell nuclei and their tracking using three dimensional (3D) microscopic images is a demanding task in many biological studies. Manual identification of nuclei centroids from images is an error-prone task, sometimes impossible to accomplish due to low contrast and the presence of noise. Nonetheless, only a few methods are available for 3D bioimaging applications, which sharply contrast with 2D analysis, where many methods already exist.