Sreetama Basu

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Automated algorithms to build accurate models of 3D neuronal arborization is much in demand due to large volume of microscopy data. We present a tracking algorithm for automatic and reliable extraction of neuronal morphology. It is robust to ambiguous branch discontinuities, variability of intensity and curvature of fibres, arbitrary branch cross-sections,(More)
Tubular structures are frequently encountered in bio-medical images. The center-lines of these tubules provide an accurate representation of the topology of the structures. We introduce a stochastic Marked Point Process framework for fully automatic extraction of tubular structures requiring no user interaction or seed points for initialization. Our Marked(More)
In this paper we present a pipeline for automatic analysis of neuronal morphology: from detection, modeling to digital reconstruction. First, we present an automatic, unsupervised object detection framework using stochastic marked point process. It extracts connected neuronal networks by fitting special configuration of marked objects to the centreline of(More)
Three-dimensional fluorescence microscopy followed by image processing is routinely used to study biological objects at various scales such as cells and tissue. However, maximum intensity projection, the most broadly used rendering tool, extracts a discontinuous layer of voxels, obliviously creating important artifacts and possibly misleading(More)
This thesis addresses the problem of automatic analysis of neuronal tree morphology: from detection, modeling to digital reconstruction of tubular branches of neurites from 3D Light Microscopy image stacks. Comprehending the complex structure and connections of the neurons is key to the study of brain development and functioning. Advances in neuroimaging(More)
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