| Differentiation of urinary stone and vascular calcifications on non-contrast CT images: an initial experience using computer aided diagnosis. | |
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MedLine Citation:
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PMID: 19190962 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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The purpose of this study was to develop methods for the differentiation of urinary stones and vascular calcifications using computer-aided diagnosis (CAD) of non-contrast computed tomography (CT) images. From May 2003 to February 2004, 56 patients that underwent a pre-contrast CT examination and subsequently diagnosed as ureter stones were included in the study. Fifty-nine ureter stones and 53 vascular calcifications on pre-contrast CT images of the patients were evaluated. The shapes of the lesions including disperseness, convex hull depth, and lobulation count were analyzed for patients with ureter stones and vascular calcifications. In addition, the internal textures including edge density, skewness, difference histogram variation (DHV), and the gray-level co-occurrence matrix moment were also evaluated for the patients. For evaluation of the diagnostic accuracy of the shape and texture features, an artificial neural network (ANN) and receiver operating characteristics curve (ROC) analyses were performed. Of the several shape factors, disperseness showed a statistical difference between ureter stones and vascular calcifications (p < 0.05). For the internal texture features, skewness and DHV showed statistical differences between ureter stones and vascular calcifications (p < 0.05). The performance of the ANN was evaluated by examining the area under the ROC curves (AUC, A (z)). The A (z) value was 0.85 for the shape parameters and 0.88 for the texture parameters. In this study, several parameters regarding shape and internal texture were statistically different between ureter stones and vascular calcifications. The use of CAD would make it possible to differentiate ureter stones from vascular calcifications by a comparison of these parameters. |
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Authors:
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Hak Jong Lee; Kwang Gi Kim; Sung Il Hwang; Seung Hyup Kim; Seok-Soo Byun; Sang Eun Lee; Seong Kyu Hong; Jeong Yeon Cho; Chang Gyu Seong |
Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't Date: 2009-02-04 |
Journal Detail:
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Title: Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology Volume: 23 ISSN: 1618-727X ISO Abbreviation: J Digit Imaging Publication Date: 2010 Jun |
Date Detail:
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Created Date: 2010-05-07 Completed Date: 2010-08-23 Revised Date: 2011-07-28 |
Medline Journal Info:
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Nlm Unique ID: 9100529 Medline TA: J Digit Imaging Country: United States |
Other Details:
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Languages: eng Pagination: 268-76 Citation Subset: IM |
Affiliation:
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Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea. |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Calcinosis
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diagnosis*,
radiography Contrast Media Diagnosis, Computer-Assisted* Female Humans Male Middle Aged Radiographic Image Interpretation, Computer-Assisted / methods* Tomography, X-Ray Computed* Urinary Calculi / diagnosis*, radiography Vascular Diseases / diagnosis*, radiography |
| Chemical | |
Reg. No./Substance:
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0/Contrast Media |
| Comments/Corrections | |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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