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Quantitative autoradiography is a powerful radioisotopic-imaging method for neuroscientists to study local cerebral blood flow and glucose-metabolic rate at rest, in response to physiologic activation of the visual, auditory, somatosensory, and motor systems, and in pathologic conditions. Most autoradiographic studies analyze glucose utilization and blood(More)
Localizing boundaries between textured image regions without sacrificing the labeling accuracy of interior regions remains a problem in segmentation. Difficulties arise because of the conflicting requirements of localization and labeling. Boundary localization usually demands observing the features over small neighborhoods, whereas labeling accuracy(More)
A mathematical model using an operator formulation for a moving object in a sequence of images is presented. Time-varying translation and rotation operators are derived to describe the motion. A variational estimation algorithm is developed to track the dynamic parameters of the operators. The occlusion problem is alleviated by using a predictive Kalman(More)
Statistical analysis of medical images in experimental laboratories plays an important role in confirming scientific findings and in guiding potential clinical applications. In experimental neuroscience studies, autoradiographic images taken under differing physiological or pathological conditions from replicate animals are often compared in order to detect(More)
The paper is concerned with the reduction of overhead storage, i.e., the stored compression/decompression (C/D) table, in field-level data file compression. A large C/D table can occupy a lage fraction of maim memory space during compression and decompression, and may cause excessve page swapping in virtual memory systems. A two-stage approach is studied,(More)