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Cloud analyses provide information which is vital to the detection, understanding and prediction of meteorological trends and environmental changes. This paper compares statistical, neural network and genetic algorithm methods for recognition and tracking of midlatitude storm clouds in sequences of low-resolution cloud-top pressure data sets. Regions of interest are identified and tracked from one image frame to the next consecutive frame in an eight-frame sequence. Classification techniques are used to determine the relationships between regions of interest in consecutive time frames. A genetic algorithm procedure is then used to revise classifier outputs to ensure that consistency constraints are not violated. © 1997 Elsevier Science B.V.
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Pores are naturally occurring entities in bone. Changes in pore size and number are often associated with diseases such as Osteoporosis and even microgravity during spaceflight. Studying bone perforations may yield great insight into bone's material properties, including bone density and may contribute to identifying therapies to halt or potentially reverse bone loss. Current technologies used in this field include nuclear magnetic resonance, micro-computed tomography and the field emission scanning electron microscope (FE-SEM) 2, 5. However, limitations in each method limit further advancement. The objective of this study was to assess the effectiveness of using a new generation of analytical instruments, the TM-1000 tabletop, SEM with back-scatter electron (BSE) detector, to analyze cortical bone porosities. Hind limb unloaded and age-based controlled mouse femurs were extracted and tested in vitro for changes in pores on the periosteal surface. An important advantage of using the tabletop is the simplified sample preparation that excludes extra coatings, dehydration and fixation steps that are otherwise required for conventional SEM. For quantitative data, pores were treated as particles in order to use an analyze particles feature in the NIH ImageJ software. Several image-processing techniques for background smoothing, thresholding and filtering were employed to produce a binary image suitable for particle analysis. It was hypothesized that the unloaded bones would show an increase in pore area, as the lack of mechanical loading would affect bone-remodeling processes taking place in and around pores. Preliminary results suggest only a slight different in frequency but not in size of pores between unloaded and control femurs.
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Nanoparticles, particles with a diameter of 1-100 nanometers (nm), are of interest in many applications including device fabrication, quantum computing, and sensing because their decreased size may give rise to certain properties that are very different from those exhibited by bulk materials. Further advancement of nanotechnology cannot be realized without an increased understanding of nanoparticle properties such as size (diameter) and size distribution. Frequently, these parameters are evaluated using numerous imaging modalities including transmission electron microscopy (TEM) and atomic force microscopy (AFM). In the past, these parameters have been obtained from digitized images by manually measuring and counting many of these nanoparticles, a task that is highly subjective and labor intensive. Recently, computer imaging particle analysis routines that count and measure objects in a binary image1 have emerged as an objective and rapid alternative to manual techniques. In this paper a procedure is described that can be used to preprocess a set of gray scale images so that they are correctly thresholded into binary images prior to a particle analysis ultimately resulting in a more accurate assessment of the size and frequency (size distribution) of nanoparticles. Particle analysis was performed on two types of calibration samples imaged using AFM and TEM. Additionally, results of particle analysis can be used for identifying and removing small noise particles from the image. This filtering technique is based on identifying the location of small particles in the binary image, assessing their size, and removing them without affecting the size of other larger particles.
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