In conventional biological imaging, diffraction places a limit on the minimal xy distance at which two marked objects can be discerned. Consequently, resolution of target molecules within cells is typically coarser by two orders of magnitude than the molecular scale at which the proteins are spatially distributed. Photoactivated localization microscopy (PALM) optically resolves selected subsets of protect fluorescent probes within cells at mean separations of <25 nanometers. It involves serial photoactivation and subsequent photobleaching of numerous sparse subsets of photoactivated fluorescent protein molecules. Individual molecules are localized at near molecular resolution by determining their centers of fluorescent emission via a statistical fit of their point-spread-function. The position information from all subsets is then assembled into a super-resolution image, in which individual fluorescent molecules are isolated at high molecular densities. In this paper, some of the limitations for PALM imaging under current experimental conditions are discussed.

Automatic segmentation of nuclei in 3D microscopy images is essential for many biological studies including high throughput analysis of gene expression level, morphology, and phenotypes in single cell level. The complexity and variability of the microscopy images present many difficulties to the traditional image segmentation methods. In this paper, we present a new method based on 3D watershed algorithm to segment such images. By using both the intensity information of the image and the geometry information of the appropriately detected foreground mask, our method is robust to intensity fluctuation within nuclei and at the same time sensitive to the intensity and geometrical cues between nuclei. Besides, the method can automatically correct potential segmentation errors by using several post-processing steps. We tested this algorithm on the 3D confocal images of C.elegans, an organism that has been widely used in biological studies. Our results show that the algorithm can segment nuclei in high accuracy despite the non-uniform background, tightly clustered nuclei with different sizes and shapes, fluctuated intensities, and hollow-shaped staining patterns in the images.

C. elegans, a roundworm in soil is widely used in studying animal development and aging, cell differentiation, etc. Recentlv, high-resolution fluorescence images of C. elegans have become available, introducing several new image analysis applications. One problem is that worm bodies usually curve greatly in images, thus it is highly desired to straighten worms so that they can be compared easily under the same canonical coordinate system. We develop a worm straightening algorithm (WSA) using a cutting-plane restacking method, which aggregates the linear rotation transforms of a continuous sequence of cutting lines/planes orthogonal to the "backbone" of a worm to best approximate the nonlinearly bended worm body. We formulate the backbone as a parametric form of cubic spline of a series of control points. We develop two minimum-spanning-tree based methods to automatically determine the locations of control points. Our experimental methods show that our approach can effectively straighten both 2D and 3D worm images.