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Institution

University of Missouri

EducationColumbia, 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.


Papers
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Journal ArticleDOI
01 Jan 2005-The Auk

376 citations

Journal ArticleDOI

375 citations

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
01 May 2000-Spine
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

NameH-indexPapersCitations
Walter C. Willett3342399413322
Meir J. Stampfer2771414283776
Russel J. Reiter1691646121010
Chad A. Mirkin1641078134254
Robert Stone1601756167901
Howard I. Scher151944101737
Rajesh Kumar1494439140830
Joseph T. Hupp14173182647
Lihong V. Wang136111872482
Stephen R. Carpenter131464109624
Jan A. Staessen130113790057
Robert S. Brown130124365822
Mauro Giavalisco12841269967
Kenneth J. Pienta12767164531
Matthew W. Gillman12652955835
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023120
2022532
20213,698
20203,683
20193,339
20183,182