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Comparison of 5 Protocols Based on Their Abilities to Use Data Extracted From Digitized Clinical Radiographs to Discriminate Between Patients With Gingivitis and Periodontitis

Background: This study was undertaken to compare 5 digital analytic protocols for their abilities to extract data from digital clinical radiographs and discriminate between patients with gingivitis and periodontitis. Methods: Five digital‐image analysis protocols were compared for their abilities to...

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Published in:Journal of periodontology (1970) 2000-11, Vol.71 (11), p.1750-1755
Main Authors: Shrout, Michael K., Hildebolt, Charles F., Potter, Brad J., Comer, Robert W.
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Hildebolt, Charles F.
Potter, Brad J.
Comer, Robert W.
description Background: This study was undertaken to compare 5 digital analytic protocols for their abilities to extract data from digital clinical radiographs and discriminate between patients with gingivitis and periodontitis. Methods: Five digital‐image analysis protocols were compared for their abilities to discriminate between two groups of 24 patients each. One group was diagnosed with healthy gingiva (or gingivitis) and the second with periodontitis. These groups were previously evaluated in published studies that used fractal and morphologic analyses. Pre‐existing clinical radiographs for each patient were digitized and regions of interest (ROIs) were placed on interdental bone in mandibular posterior quadrants. The 5 protocols used were: 1) MGB: a median filtration to remove high‐frequency noise, a Gaussian filtration to remove low‐frequency noise, binarization of the resulting image, and quantification of the black pixels; 2) MGBS: the same protocol as MGB except for a skeletonization of the binary image and a quantification of the skeleton's pixels; 3) GBS: Gaussian filtration, binarization (thresholding on the mean pixel value) of the resulting image, skeletonization, and quantification of the pixels of the skeleton; 4) NS: normalization, skeletonization, and quantification of the skeleton's pixels; and 5) S: a variation of NS, except normalization was not used. The resulting values for the 2 patient groups were compared with Mann‐Whitney U tests and effect likelihood‐ratio test. Results: For digitized radiographs, the mean gray‐scale value (± standard deviation) for gingivitis patients was 183.22 ± 18.53 and for periodontitis patients 181.26 ± 17.20. Mann‐Whitney U tests resulted in the following P values for these protocols: MGBS
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Methods: Five digital‐image analysis protocols were compared for their abilities to discriminate between two groups of 24 patients each. One group was diagnosed with healthy gingiva (or gingivitis) and the second with periodontitis. These groups were previously evaluated in published studies that used fractal and morphologic analyses. Pre‐existing clinical radiographs for each patient were digitized and regions of interest (ROIs) were placed on interdental bone in mandibular posterior quadrants. The 5 protocols used were: 1) MGB: a median filtration to remove high‐frequency noise, a Gaussian filtration to remove low‐frequency noise, binarization of the resulting image, and quantification of the black pixels; 2) MGBS: the same protocol as MGB except for a skeletonization of the binary image and a quantification of the skeleton's pixels; 3) GBS: Gaussian filtration, binarization (thresholding on the mean pixel value) of the resulting image, skeletonization, and quantification of the pixels of the skeleton; 4) NS: normalization, skeletonization, and quantification of the skeleton's pixels; and 5) S: a variation of NS, except normalization was not used. The resulting values for the 2 patient groups were compared with Mann‐Whitney U tests and effect likelihood‐ratio test. Results: For digitized radiographs, the mean gray‐scale value (± standard deviation) for gingivitis patients was 183.22 ± 18.53 and for periodontitis patients 181.26 ± 17.20. Mann‐Whitney U tests resulted in the following P values for these protocols: MGBS &lt;0.01; S &lt;0.01; GBS &lt;0.01; NS &lt;0.01; and MGB &lt;0.83. Effect likelihood‐ratio tests indicated that only MGBS and S significantly contributed to models containing the other factors. Conclusions: Small variations to protocols affected the strength of the discrimination between the gingivitis and periodontitis groups. While there is potential for morphologic analysis to be used to discriminate between patients with gingivitis and periodontitis, a robust technique was not identified. J Periodontol 2000;71:1750‐1755.</description><identifier>ISSN: 0022-3492</identifier><identifier>EISSN: 1943-3670</identifier><identifier>DOI: 10.1902/jop.2000.71.11.1750</identifier><identifier>PMID: 11128924</identifier><language>eng</language><publisher>737 N. Michigan Avenue, Suite 800, Chicago, IL 60611‐2690, USA: American Academy of Periodontology</publisher><subject>Adult ; Alveolar Process - diagnostic imaging ; Comparison studies ; Dentistry ; Diagnosis, Differential ; Female ; Filtration - instrumentation ; Gingivitis - diagnostic imaging ; gingivitis/radiography ; Humans ; Likelihood Functions ; Male ; Periodontitis - diagnostic imaging ; periodontitis/radiography ; Radiographic Image Enhancement - methods ; Radiography, Dental - methods ; radiography, dental, digital ; Statistics, Nonparametric</subject><ispartof>Journal of periodontology (1970), 2000-11, Vol.71 (11), p.1750-1755</ispartof><rights>2000 American Academy of Periodontology</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3510-2d0d490c8fd0ceb21a445d7b699960815c9ab418ec389d2b7b3478d22e916ff3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27922,27923</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/11128924$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shrout, Michael K.</creatorcontrib><creatorcontrib>Hildebolt, Charles F.</creatorcontrib><creatorcontrib>Potter, Brad J.</creatorcontrib><creatorcontrib>Comer, Robert W.</creatorcontrib><title>Comparison of 5 Protocols Based on Their Abilities to Use Data Extracted From Digitized Clinical Radiographs to Discriminate Between Patients With Gingivitis and Periodontitis</title><title>Journal of periodontology (1970)</title><addtitle>J Periodontol</addtitle><description>Background: This study was undertaken to compare 5 digital analytic protocols for their abilities to extract data from digital clinical radiographs and discriminate between patients with gingivitis and periodontitis. Methods: Five digital‐image analysis protocols were compared for their abilities to discriminate between two groups of 24 patients each. One group was diagnosed with healthy gingiva (or gingivitis) and the second with periodontitis. These groups were previously evaluated in published studies that used fractal and morphologic analyses. Pre‐existing clinical radiographs for each patient were digitized and regions of interest (ROIs) were placed on interdental bone in mandibular posterior quadrants. The 5 protocols used were: 1) MGB: a median filtration to remove high‐frequency noise, a Gaussian filtration to remove low‐frequency noise, binarization of the resulting image, and quantification of the black pixels; 2) MGBS: the same protocol as MGB except for a skeletonization of the binary image and a quantification of the skeleton's pixels; 3) GBS: Gaussian filtration, binarization (thresholding on the mean pixel value) of the resulting image, skeletonization, and quantification of the pixels of the skeleton; 4) NS: normalization, skeletonization, and quantification of the skeleton's pixels; and 5) S: a variation of NS, except normalization was not used. The resulting values for the 2 patient groups were compared with Mann‐Whitney U tests and effect likelihood‐ratio test. Results: For digitized radiographs, the mean gray‐scale value (± standard deviation) for gingivitis patients was 183.22 ± 18.53 and for periodontitis patients 181.26 ± 17.20. Mann‐Whitney U tests resulted in the following P values for these protocols: MGBS &lt;0.01; S &lt;0.01; GBS &lt;0.01; NS &lt;0.01; and MGB &lt;0.83. Effect likelihood‐ratio tests indicated that only MGBS and S significantly contributed to models containing the other factors. Conclusions: Small variations to protocols affected the strength of the discrimination between the gingivitis and periodontitis groups. While there is potential for morphologic analysis to be used to discriminate between patients with gingivitis and periodontitis, a robust technique was not identified. J Periodontol 2000;71:1750‐1755.</description><subject>Adult</subject><subject>Alveolar Process - diagnostic imaging</subject><subject>Comparison studies</subject><subject>Dentistry</subject><subject>Diagnosis, Differential</subject><subject>Female</subject><subject>Filtration - instrumentation</subject><subject>Gingivitis - diagnostic imaging</subject><subject>gingivitis/radiography</subject><subject>Humans</subject><subject>Likelihood Functions</subject><subject>Male</subject><subject>Periodontitis - diagnostic imaging</subject><subject>periodontitis/radiography</subject><subject>Radiographic Image Enhancement - methods</subject><subject>Radiography, Dental - methods</subject><subject>radiography, dental, digital</subject><subject>Statistics, Nonparametric</subject><issn>0022-3492</issn><issn>1943-3670</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2000</creationdate><recordtype>article</recordtype><recordid>eNqNkc9qGzEQh0VpaNy0T1AoOvW2jqTVelfHxHb-lEBNcOlRaKVZW2FX2kpy0vSl-oqRY0OvgQExwzcfI34IfaFkSgVh5w9-nDJCyLSmU5qrrsg7NKGCl0U5q8l7NCGEsaLkgp2ijzE-5JbyknxAp5RS1gjGJ-jf3A-jCjZ6h32HK7wKPnnt-4gvVQSD83y9BRvwRWt7myxEnDz-GQEvVFJ4-ScFpVMGr4If8MJuMvM3t_PeOqtVj--VsX4T1Lh93VzYqIMdrFMJ8CWkJwCHVyqLXYr4l01bfG3dxj5mT8TKGbyCYL3xLu0nn9BJp_oIn4_vGVpfLdfzm-Lux_Xt_OKu0GVFScEMMVwQ3XSGaGgZVZxXpm5nQogZaWilhWo5bUCXjTCsrduS141hDASddV15hr4dtGPwv3cQkxzy3dD3yoHfRVkzLhrKqwyWB1AHH2OATo75dyo8S0rkPiaZY5L7mGRNJc2VY8pbX4_6XTuA-b9zzCUD4gA82R6e3-KU31fL-1f5C9J6otA</recordid><startdate>200011</startdate><enddate>200011</enddate><creator>Shrout, Michael K.</creator><creator>Hildebolt, Charles F.</creator><creator>Potter, Brad J.</creator><creator>Comer, Robert W.</creator><general>American Academy of Periodontology</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>200011</creationdate><title>Comparison of 5 Protocols Based on Their Abilities to Use Data Extracted From Digitized Clinical Radiographs to Discriminate Between Patients With Gingivitis and Periodontitis</title><author>Shrout, Michael K. ; Hildebolt, Charles F. ; Potter, Brad J. ; Comer, Robert W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3510-2d0d490c8fd0ceb21a445d7b699960815c9ab418ec389d2b7b3478d22e916ff3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Adult</topic><topic>Alveolar Process - diagnostic imaging</topic><topic>Comparison studies</topic><topic>Dentistry</topic><topic>Diagnosis, Differential</topic><topic>Female</topic><topic>Filtration - instrumentation</topic><topic>Gingivitis - diagnostic imaging</topic><topic>gingivitis/radiography</topic><topic>Humans</topic><topic>Likelihood Functions</topic><topic>Male</topic><topic>Periodontitis - diagnostic imaging</topic><topic>periodontitis/radiography</topic><topic>Radiographic Image Enhancement - methods</topic><topic>Radiography, Dental - methods</topic><topic>radiography, dental, digital</topic><topic>Statistics, Nonparametric</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shrout, Michael K.</creatorcontrib><creatorcontrib>Hildebolt, Charles F.</creatorcontrib><creatorcontrib>Potter, Brad J.</creatorcontrib><creatorcontrib>Comer, Robert W.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of periodontology (1970)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shrout, Michael K.</au><au>Hildebolt, Charles F.</au><au>Potter, Brad J.</au><au>Comer, Robert W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of 5 Protocols Based on Their Abilities to Use Data Extracted From Digitized Clinical Radiographs to Discriminate Between Patients With Gingivitis and Periodontitis</atitle><jtitle>Journal of periodontology (1970)</jtitle><addtitle>J Periodontol</addtitle><date>2000-11</date><risdate>2000</risdate><volume>71</volume><issue>11</issue><spage>1750</spage><epage>1755</epage><pages>1750-1755</pages><issn>0022-3492</issn><eissn>1943-3670</eissn><abstract>Background: This study was undertaken to compare 5 digital analytic protocols for their abilities to extract data from digital clinical radiographs and discriminate between patients with gingivitis and periodontitis. Methods: Five digital‐image analysis protocols were compared for their abilities to discriminate between two groups of 24 patients each. One group was diagnosed with healthy gingiva (or gingivitis) and the second with periodontitis. These groups were previously evaluated in published studies that used fractal and morphologic analyses. Pre‐existing clinical radiographs for each patient were digitized and regions of interest (ROIs) were placed on interdental bone in mandibular posterior quadrants. The 5 protocols used were: 1) MGB: a median filtration to remove high‐frequency noise, a Gaussian filtration to remove low‐frequency noise, binarization of the resulting image, and quantification of the black pixels; 2) MGBS: the same protocol as MGB except for a skeletonization of the binary image and a quantification of the skeleton's pixels; 3) GBS: Gaussian filtration, binarization (thresholding on the mean pixel value) of the resulting image, skeletonization, and quantification of the pixels of the skeleton; 4) NS: normalization, skeletonization, and quantification of the skeleton's pixels; and 5) S: a variation of NS, except normalization was not used. The resulting values for the 2 patient groups were compared with Mann‐Whitney U tests and effect likelihood‐ratio test. Results: For digitized radiographs, the mean gray‐scale value (± standard deviation) for gingivitis patients was 183.22 ± 18.53 and for periodontitis patients 181.26 ± 17.20. Mann‐Whitney U tests resulted in the following P values for these protocols: MGBS &lt;0.01; S &lt;0.01; GBS &lt;0.01; NS &lt;0.01; and MGB &lt;0.83. Effect likelihood‐ratio tests indicated that only MGBS and S significantly contributed to models containing the other factors. Conclusions: Small variations to protocols affected the strength of the discrimination between the gingivitis and periodontitis groups. While there is potential for morphologic analysis to be used to discriminate between patients with gingivitis and periodontitis, a robust technique was not identified. J Periodontol 2000;71:1750‐1755.</abstract><cop>737 N. Michigan Avenue, Suite 800, Chicago, IL 60611‐2690, USA</cop><pub>American Academy of Periodontology</pub><pmid>11128924</pmid><doi>10.1902/jop.2000.71.11.1750</doi><tpages>6</tpages></addata></record>
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source Wiley-Blackwell Read & Publish Collection
subjects Adult
Alveolar Process - diagnostic imaging
Comparison studies
Dentistry
Diagnosis, Differential
Female
Filtration - instrumentation
Gingivitis - diagnostic imaging
gingivitis/radiography
Humans
Likelihood Functions
Male
Periodontitis - diagnostic imaging
periodontitis/radiography
Radiographic Image Enhancement - methods
Radiography, Dental - methods
radiography, dental, digital
Statistics, Nonparametric
title Comparison of 5 Protocols Based on Their Abilities to Use Data Extracted From Digitized Clinical Radiographs to Discriminate Between Patients With Gingivitis and Periodontitis
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