Indexing of scientific image databases is a difficult task, due their extraordinary sizes and to the complex nature of the visual information contained in them. This data volume and complexity require an automatic indexing scheme that will categorize this visual content; without it, the data will be essentially useless to scientists and medical doctors. A method for automatic indexing of scientific image databases is presented which involves a wavelet packet decomposition of images in the frequency domain, resulting in a quad-tree of subbands. These subbands are regarded as realizations of random fields, and statistical measures are computed on them. One class of newly derived measures determines whether the subbands contain any significant organization of pixels beyond what chance would imply. If this is found to be true for a subband, its node is retained on an index tree, and other identifying measurements may be added. The structure of the resulting pruned subband tree constitutes the first level of index; the node statistics form a second indexing level. Results of a pilot study are reported; they suggest that further investigation of this approach is warranted.
Prior research has demonstrated that the quality of moderation and management in online communities of practice is key to their successful support of learning. However, as communities grow in size and complexity, it becomes increasingly difficult for unaided experts to fully understand and take action in response to the activity of participants within them. Learning analytics has the potential to provide the support that community of practice leaders need to improve their performance. The National Science Teachers Association and U.S. Department of Education's Connected Educators project are exploring three approaches for managing forums to make them accessible and to synthesize the knowledge they generate: archiving, summarizing, and reorganizing. This paper describes manual heuristics for the first two of these, as well as the use of social network analysis to help develop algorithms to automate the third, community forum reorganization.
The demand for digital radiological imaging and archiving applications has been increasing rapidly. These digital applications offer significant advantages to the physician over the traditional film-based technique. They result in faster and better quality services, support remote access and conferencing capabilities, provide on demand service availability, eliminate film processing costs, and most sigiificantly, they are suitable services for the evolving global information supper highway . Several existing medical multimedia systems incorporate and utilize those advanced technical features. However, radiologists are seeking an order of magnitude improvement in the overall current system design and performance indices (such as transactions response times, system utilization and throughput). One of the main technical concern radiologists are raising is the miss-filing occurrence. This event s,ill decrease the radiologist prothctiviy, intrOdUce unnecessarily workload and will result in total customer dissatisfaction. This paper presents Multimedii Medical Archiving System (MMAS), which can be used in hospitals and medical centers for storing and retrieving radiological images. Furthermore, this paper emphasises a viable solution for the miss-filing problem. The results obtained demonstrate and quantify the improvement in the overall radiological operations. Specifically this paper demonstrates an order of 80% improvement in the response time for retrieving images. This enhancement in system performance directly translates to a tremendous improvement in the radiologist 's productivity.
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