Effects Of Image Resolution Research Articles

Effects of Image Resolution on Autoradiographic Measurements of Posterior Cingulate Activity in PDAPP Mice: Implications for Functional Brain Imaging Studies of Transgenic Mouse Models of Alzheimer's Disease

Fluorodeoxyglucosepositron emission tomography (PET) studies find that persons with Alzheimer's disease have preferential reductions in posterior cingulate activity. Using fluorodeoxyglucose autoradiography, we found that transgenic mice overexpressing a mutant form of the human amyloid precursor protein have preferentially reduced activity in the same region, providing a potential brain imaging indicator of Alzheimer's disease in these animals. In this study, we considered the feasibility of using in vivo imaging techniques, such as PET, to detect this reduction despite their limitations in spatial resolution. Autoradiographic measurements of posterior cingulate activity were remeasured in the previously studied PDAPP transgenic and littermate control mice after the images were filtered to lower spatial resolutions. We continued to detect significantly lower cingulate activity in the transgenic mice when the images were blurred to 0.50 mm, failed to detect significantly abnormal activity when the images were blurred to 0.75 mm, and, indeed, found significantly higher activity when the images were blurred to 1.0 mm. Reversal in direction of the abnormality appears attributable to a previously observed truncation in the corpus callosum in PDAPP mice. With the possible exception of future in vivo imaging techniques that have a spatial resolution greater than or equal to 0.50 mm and high sensitivity, noninvasive functional brain imaging techniques like PET may not be suitable for detecting declines in regional activity in PDAPP mice. It remains possible that these imaging techniques will prove useful in transgenic mouse lines that do not exhibit the same morphological abnormalities in neighboring white matter regions.

Scale-invariant analysis of wear particle surface morphology: III: Pattern recognition

A new scale-invariant pattern recognition method has been developed for the characterization of wear particle surface morphology. The method is based on a partition-iterated function system (PIFS) constructed for a wear particle image. A database containing PIFSs constructed for different classes of wear particles was built. Each class is a set of wear particles exhibiting a similar morphology. The objective of pattern recognition is to assign a new wear particle, which is not yet classified, into one of these particle classes. For the classification, PIFSs taken from the database are decoded with the unclassified particle image used as an initial image. Decoded images obtained from PIFSs are then individually compared with the new particle image. If the difference calculated between these images is less than a threshold value, the new particle is assigned to a class. Problems associated with the use of the pattern recognition method, such as rotational- and translation-invariance, comparison between two images and computer implementation, are addressed in this paper. The effects of image resolution, tilt angle, noise, gain variations and defocusing were also investigated. Results obtained for computer-generated images of fractal surfaces and microscopic images of wear particles are demonstrated.