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We present a region based method for segmenting and splitting
images of cells in an automatic and unsupervised manner.
The detection of cell nuclei is based on the Bradley’s method.
False positives are automatically identified and rejected based
on shape and intensity features. Additionally, the proposed
method is able to automatically detect and split touching cells.
To do so, we employ a variant of a region based multi-ellipse
fitting method (DEFA) that makes use of constraints on the

This work proposes a volumetric data restoration method, especially for data acquired through an optical coherence tomography (OCT) device. OCT is a technique for acquiring a tomographic image of a specimen object in a few $\mu$m scale by using a near infrared laser. The authors have been trying dynamic observation of epithelium in cochlear of the inner ear. Currently, there is a problem to remove the influence of the measurement process as well as noise due to image sensor sensitivity.

In microscopy, new super-resolution methods are emerging that produce three-dimensional images at resolutions ten times smaller than that provided by traditional light microscopy. Such technology is enabling the exploration of structure and function in living tissues such as bacterial biofilms that have mysterious interconnections and organization. Unfortunately, the standard tools used in the image analysis community to perform segmentation and other higher-level analyses cannot be applied naively to these data.

Blur estimation is critical to blind image deconvolution. In this work, by taking Gaussian kernel as an example, we propose an approach to estimate the blur size for photon-limited images. This estimation is based on the minimization of a novel criterion, blur-PURE (Poisson unbiased risk estimate), which makes use of the Poisson noise statistics of the measurement. Experimental results demonstrate the effectiveness of the proposed method in various scenarios.

Significant cardiac and respiratory motion of the living subject, occasional spells of defocus, drifts in the field of view,
and long image sequences make the registration of in-vivo microscopy image sequences used in atherosclerosis study an
onerous task. In this study we developed and implemented a novel Minimum Spanning Tree (MST)-based clustering
method for image sequence registration that first constructs a minimum spanning tree for the input image sequence. The

Neurons depend critically on active transport of cargoes throughout their complex neurite networks for their survival and function. Defects in this process have been strongly associated with many human neurodevelopmental and neurodegenerative diseases. To understand related neuronal physiology and disease mechanisms, it is essential to measure the traffic flow within the neurite networks. Currently, however, image analysis methods required for this measurement are lacking.

Based on the dynamic structure, we design a system that can perform accurate stromule image segmentation, branch tip detection and tracking automatically. We substitute the user constraints in active contour segmentation by spatial fuzzy c-means clustering for providing more precise segmentation result. Based on the segmented contour after smoothing, we create a surface normal based feature that can accurately detect
the branch tips. We further combine normal information together with tip position coordinate to apply ICP to track the branch tips moving path.