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The challenges faced in analyzing optical imaging data from neurons include a low signal-to-noise ratio of the acquired images and the multiscale nature of the tubular structures that range in size from hundreds of microns to hundreds of nanometers. In this paper, we address these challenges and present a computational framework for an automatic, three-dimensional (3D) morphological reconstruction...
We present a model for the automated segmentation of cells from confocal microscopy volumes of biological samples. The segmentation task for these images is exceptionally challenging due to weak boundaries and varying intensity during the imaging process. To tackle this, a two step pruning process based on the Fast Marching Method is first applied to obtain an over-segmented image. This is followed...
A critical part in our knowledge of the brain is the understanding of the structure and morphology of neurons. In recent years, we have witnessed a tremendous effort in developing libraries of neuronal structures that can be used for multiple purposes. These critical studies include modeling the brain connectivity and understanding how cellular structure regulates brain function. Presently, methods...
In vivo observation and tracking of cell division in the Arabidopsis thaliana root meristem, by time-lapse confocal microscopy, is central to biology research. The research herein described is based on large amount of image data, which must be analyzed to determine the location and state of cells. The possibility of automating the process of cell detection/marking is an important step to provide research...
Confocal microscopy can provide sequences of Images from all cornea layers in a rapid, in vivo and non invasive way. These images are useful to extract important clinical information on cornea state of health. We address the problem of obtaining a 3-dimensional (3D) reconstruction of the cornea starting from a confocal microscope sequence, from endothelium to epithelium. A registration procedure,...
The essence and methodology is proposed for interactive visualization of confocal microscopy images. Firstly, an intensity compensation algorithm was applied to reduce noises resulted from light absorption and scattering by objects and particles in the volume through which light passed. Secondly, a deconvolution algorithm based on Maximum A Posteriori was applied to improve the image stacks' quality...
Brain tissue is the most complex tissue in the mammalian anatomy. It is structurally complex, containing diverse cell types and vasculature with complex spatial relationships. It is also complex in terms of molecular constituents, and their relationship to the structural constituents. There is a need for technologies to map normal and injured brain tissue and compute appropriate quantitative measurements...
This paper presents an image segmentation method based on support vector machines classifiers at a pixel level. We apply this method to quantify the amount of Mycobacterium tuberculosis in confocal microscopy images for drug-discovery within the context of high content screening (HCS). To deal with the performance constraints of HCS, we propose a model-selection algorithm that finds the best classifier's...
Tagging and tracking protein compounds/compounds are key to a better understanding of proteomics such as protein-protein interaction and protein signaling pathway. In this paper, a generalized region tracking framework by statistical particle filter (PF) is presented for tracing the movement of protein compounds in confocal microscopy images. To effectively select the features to be tracked, a grid-based...
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