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Description:
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Multiphoton laser scanning microscopy (MPLSM ) is an advanced fluorescence
imaging technology which can produce a less noisy microscope image and minimize the
damage in living tissue . The MPLSM image in this research is the dehydroergosterol
(DHE , a fluorescent sterol which closely mimics those of cholesterol in lipoproteins and
membranes ) on living cell's plasma membrane area . The objective is to use a statistical
image analysis method to describe how cholesterol is distributed on a living cell's
membrane . Statistical image analysis methods applied in this research include image
segmentation /classification and spatial analysis . In image segmentation analysis , we
design a supervised learning method by using smoothing technique with rank statistics .
This approach is especially useful in a situation where we have only very limited
information of classes we want to segment . We also apply unsupervised leaning methods
on the image data . In image data spatial analysis , we explore the spatial correlation of
segmented data by a Monte Carlo test . Our research shows that the distributions of DHE
exhibit a spatially aggregated pattern . We fit two aggregated point pattern models , an
area -interaction process model and a Poisson cluster process model , to the data . For the area interaction process model , we design algorithms for maximum pseudo -likelihood
estimator and Monte Carlo maximum likelihood estimator under lattice data setting . For
the Poisson Cluster process parameter estimation , the method for implicit statistical
model parameter estimate is used . A group of simulation studies shows that the Monte
Carlo maximum estimation method produces consistent parameter estimates . The
goodness -of -fit tests show that we cannot reject both models . We propose to use the area
interaction process model in further research . |