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Your search keyword '"Noseworthy, Michael D."' showing total 12 results
Start Over Author "Noseworthy, Michael D." Journal critical reviews in biomedical engineering
12 results on '"Noseworthy, Michael D."'

9. IN MEMORIAM: SKIPPER POEHLMAN

Author

Kamath, Markad V., primary, Boylan, Colm, additional, Jones, Graham, additional, Kahl, Wolfram, additional, Noseworthy, Michael D., additional, and Upton, Adrian R. M., additional

Published
2014
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12. Segmentation of images of abdominal organs.

Author

Wu J, Kamath MV, Noseworthy MD, Boylan C, and Poehlman S

Subjects
Artificial Intelligence, Computer Simulation, Humans, Image Enhancement methods, Imaging, Three-Dimensional methods, Models, Biological, Reproducibility of Results, Sensitivity and Specificity, Abdomen anatomy & histology, Algorithms, Diagnostic Imaging methods, Image Interpretation, Computer-Assisted methods, Pattern Recognition, Automated methods, Subtraction Technique, Viscera anatomy & histology
Abstract

Abdominal organ segmentation, which is, the delineation of organ areas in the abdomen, plays an important role in the process of radiological evaluation. Attempts to automate segmentation of abdominal organs will aid radiologists who are required to view thousands of images daily. This review outlines the current state-of-the-art semi-automated and automated methods used to segment abdominal organ regions from computed tomography (CT), magnetic resonance imaging (MEI), and ultrasound images. Segmentation methods generally fall into three categories: pixel based, region based and boundary tracing. While pixel-based methods classify each individual pixel, region-based methods identify regions with similar properties. Boundary tracing is accomplished by a model of the image boundary. This paper evaluates the effectiveness of the above algorithms with an emphasis on their advantages and disadvantages for abdominal organ segmentation. Several evaluation metrics that compare machine-based segmentation with that of an expert (radiologist) are identified and examined. Finally, features based on intensity as well as the texture of a small region around a pixel are explored. This review concludes with a discussion of possible future trends for abdominal organ segmentation.

Published
2008
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