Institution
University of Missouri
Education•Columbia, Missouri, United States•
About: University of Missouri is a education organization based out in Columbia, Missouri, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 41427 authors who have published 83598 publications receiving 2911437 citations. The organization is also known as: Mizzou & Missouri-Columbia.
Topics: Population, Poison control, Gene, Context (language use), Health care
Papers published on a yearly basis
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376 citations
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375 citations
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TL;DR: An object-based approach for urban land cover classification from high-resolution multispectral image data that builds upon a pixel-based fuzzy classification approach is presented and is able to identify buildings, impervious surface, and roads in dense urban areas with 76, 81, and 99% classification accuracies.
Abstract: In this paper, we present an object-based approach for urban land cover classification from high-resolution multispectral image data that builds upon a pixel-based fuzzy classification approach. This combined pixel/object approach is demonstrated using pan-sharpened multispectral IKONOS imagery from dense urban areas. The fuzzy pixel-based classifier utilizes both spectral and spatial information to discriminate between spectrally similar road and building urban land cover classes. After the pixel-based classification, a technique that utilizes both spectral and spatial heterogeneity is used to segment the image to facilitate further object-based classification. An object-based fuzzy logic classifier is then implemented to improve upon the pixel-based classification by identifying one additional class in dense urban areas: nonroad, nonbuilding impervious surface. With the fuzzy pixel-based classification as input, the object-based classifier then uses shape, spectral, and neighborhood features to determine the final classification of the segmented image. Using these techniques, the object-based classifier is able to identify buildings, impervious surface, and roads in dense urban areas with 76%, 81%, and 99% classification accuracies, respectively.
375 citations
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TL;DR: In this article, the authors review linkages to optimal interpolation, kriging, Kalman filtering, smoothing, and variational analysis for data assimilation in Bayesian statistics.
375 citations
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TL;DR: It is demonstrated that grading comminution by use of the Load-Sharing Classification for approach selection and the choice of patients with isolated fractures who are cooperative with spinal bracing for 4 months provide the keys to successful short-segment treatment of isolated spinal fractures.
Abstract: Study design A retrospective review of all the surgically managed spinal fractures at the University of Missouri Medical Center during the 41/2-year period from January 1989 to July 1993 was performed. Of the 51 surgically managed patients, 46 were instrumented by short-segment technique (attachment of one level above the fracture to one level below the fracture). The other 5 patients in this consecutive series had multiple trauma. These patients were included in the review because this was a consecutive series. However, they were grouped separately because they were instrumented by long-segment technique because of their multiple organ system injuries. Objectives The choice of the anterior or posterior approach for short-segment instrumentation was based on the Load-Sharing Classification published in a 1994 issue of Spine. The purpose of this review was to demonstrate that grading comminution by use of the Load-Sharing Classification for approach selection and the choice of patients with isolated fractures who are cooperative with spinal bracing for 4 months provide the keys to successful short-segment treatment of isolated spinal fractures. Summary of background data The current literature implies that the use of pedicle screws for short-segment instrumentation of spinal fracture is dangerous and inappropriate because of the high screw fracture rate. Methods Charts, operative notes, preoperative and postoperative radiographs, computed tomography scans, and follow-up records of all patients were reviewed carefully from the time of surgery until final follow-up assessment. The Load-Sharing Classification had been used prospectively for all patients before their surgery to determine the approach for short-segment instrumentation. Denis' Pain Scale and Work Scales were obtained during follow-up evaluation for all patients. Results All patients were observed over 40 months except for 1 patient who died of unrelated causes after 35 months. The mean follow-up period was 66 months (51/2 years). No patient was lost to follow-up evaluation. Prospective application of the Load-Sharing Classification to the patients' injury and restriction of the short-segment approach to cooperative patients with isolated spinal fractures (excluding multisystem trauma patients) allowed 45 of 46 patients instrumented by the short-segment technique to proceed to successful healing in virtual anatomic alignment. Conclusions The Load-Sharing Classification is a straightforward way to describe the amount of bony comminution in a spinal fracture. When applied to patients with isolated spine fractures who are cooperative with 3 to 4 months of spinal bracing, it can help the surgeon select short-segment pedicle-screw-based fixation using the posterior approach for less comminuted injuries and the anterior approach for those more comminuted. The choice of which fracture-dislocations should be strut grafted anteriorly and which need only posterior short-segment pedicle-screw-based instrumentation also can be made using the Load-Sharing Classification.
375 citations
Authors
Showing all 41750 results
Name | H-index | Papers | Citations |
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Walter C. Willett | 334 | 2399 | 413322 |
Meir J. Stampfer | 277 | 1414 | 283776 |
Russel J. Reiter | 169 | 1646 | 121010 |
Chad A. Mirkin | 164 | 1078 | 134254 |
Robert Stone | 160 | 1756 | 167901 |
Howard I. Scher | 151 | 944 | 101737 |
Rajesh Kumar | 149 | 4439 | 140830 |
Joseph T. Hupp | 141 | 731 | 82647 |
Lihong V. Wang | 136 | 1118 | 72482 |
Stephen R. Carpenter | 131 | 464 | 109624 |
Jan A. Staessen | 130 | 1137 | 90057 |
Robert S. Brown | 130 | 1243 | 65822 |
Mauro Giavalisco | 128 | 412 | 69967 |
Kenneth J. Pienta | 127 | 671 | 64531 |
Matthew W. Gillman | 126 | 529 | 55835 |