Gregg M. Ridder

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Automated three-dimensional (3-D) image analysis methods are presented for rapid and effective analysis of populations of fluorescently labeled cells or nuclei in thick tissue sections that have been imaged three dimensionally using a confocal microscope. The methods presented here greatly improve upon our earlier work (Roysam et al.:J Microsc 173: 115-126,(More)
Routine histopathologic evaluation of the brain (paraffin embedding, hematoxylin and eosin staining) makes it difficult for an investigator to identify the overall location and relative extent of lesions as they relate to neural substructures. Moreover, it is very difficult to convey this information to others who are less familiar with neuroanatomy. This(More)
Biomedical image analysis has always been difficult due to the non-homogeneity of biological samples. Overly specific algorithms can often only be applied to one special case and high computational requirements for more complex methods also mean that large image stacks cannot be processed efficiently. We have designed a statistical method to count the(More)
We have developed an automated image analysis system that provides comparable classification of morphologically transformed SHE cell colonies to the current visual classification method used in the in vitro SHE cell transformation assay. Visual classification of morphologic transformation in this assay has been shown to accurately predict the carcinogenic(More)
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