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.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/4412 |
| Date | 30 October 2006 |
| Creators | Zhang, Weimin |
| Contributors | Wang, Suojin |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 1091727 bytes, electronic, application/pdf, born digital |
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