A method based on moving least squares for XRII image distortion correction
This paper presents a novel integrated method to correct geometric distortions of XRII (x-ray image intensifier) images. The method has been compared, in terms of mean-squared residual error measured at control and intermediate points, with two traditional local methods and a traditional global meth...
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Veröffentlicht in: | Medical physics (Lancaster) 2007-11, Vol.34 (11), p.4194-4206 |
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description | This paper presents a novel integrated method to correct geometric distortions of XRII (x-ray image intensifier) images. The method has been compared, in terms of mean-squared residual error measured at control and intermediate points, with two traditional local methods and a traditional global methods. The proposed method is based on the methods of moving least squares (MLS) and polynomial fitting. Extensive experiments were performed on simulated and real XRII images. In simulation, the effect of pincushion distortion, sigmoidal distortion, local distortion, noise, and the number of control points was tested. The traditional local methods were sensitive to pincushion and sigmoidal distortion. The traditional global method was only sensitive to sigmoidal distortion. The proposed method was found neither sensitive to pincushion distortion nor sensitive to sigmoidal distortion. The sensitivity of the proposed method to local distortion was lower than or comparable with that of the traditional global method. The sensitivity of the proposed method to noise was higher than that of all three traditional methods. Nevertheless, provided the standard deviation of noise was not greater than 0.1 pixels, accuracy of the proposed method is still higher than the traditional methods. The sensitivity of the proposed method to the number of control points was greatly lower than that of the traditional methods. Provided that a proper cutoff radius is chosen, accuracy of the proposed method is higher than that of the traditional methods. Experiments on real images, carried out by using a 9 in. XRII, showed that residual error of the proposed method
(
0.2544
±
0.2479
pixels
)
is lower than that of the traditional global method
(
0.4223
±
0.3879
pixels
)
and local methods (
0.4555
±
0.3518
pixels
and
0.3696
±
0.4019
pixels
, respectively). |
doi_str_mv | 10.1118/1.2791037 |
format | Article |
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(
0.2544
±
0.2479
pixels
)
is lower than that of the traditional global method
(
0.4223
±
0.3879
pixels
)
and local methods (
0.4555
±
0.3518
pixels
and
0.3696
±
0.4019
pixels
, respectively).</description><identifier>ISSN: 0094-2405</identifier><identifier>EISSN: 2473-4209</identifier><identifier>DOI: 10.1118/1.2791037</identifier><identifier>PMID: 18072483</identifier><identifier>CODEN: MPHYA6</identifier><language>eng</language><publisher>United States: American Association of Physicists in Medicine</publisher><subject>aberrations ; ACCURACY ; Algorithms ; BIOMEDICAL RADIOGRAPHY ; Calibration ; Computer Simulation ; CORRECTIONS ; diagnostic radiography ; distortion correction ; fluoroscopic image ; Fluoroscopy - methods ; General statistical methods ; Humans ; Image analysis ; IMAGE INTENSIFIERS ; Image Processing, Computer-Assisted - methods ; least mean squares methods ; LEAST SQUARE FIT ; least squares ; Least-Squares Analysis ; Medical image distortion ; Medical imaging ; Medical X‐ray imaging ; Models, Statistical ; moving least squares ; NOISE ; polynomial approximation ; POLYNOMIALS ; Radiographic Image Interpretation, Computer-Assisted ; RADIOLOGY AND NUCLEAR MEDICINE ; Reproducibility of Results ; SENSITIVITY ; Sensitivity and Specificity ; Signal generators ; Software ; X-RAY RADIOGRAPHY ; X-Rays ; XRII ; X‐ and γ‐ray instruments ; X‐ray imaging ; X‐ray optics</subject><ispartof>Medical physics (Lancaster), 2007-11, Vol.34 (11), p.4194-4206</ispartof><rights>American Association of Physicists in Medicine</rights><rights>2007 American Association of Physicists in Medicine</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4197-f358a7b5e5da27c2db7142b508c8ec8c6c560088841d8b7d810901bfa9d098c63</citedby><cites>FETCH-LOGICAL-c4197-f358a7b5e5da27c2db7142b508c8ec8c6c560088841d8b7d810901bfa9d098c63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1118%2F1.2791037$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1118%2F1.2791037$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,780,784,885,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/18072483$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/biblio/21032830$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Yan, Shiju</creatorcontrib><creatorcontrib>Wang, Chengtao</creatorcontrib><creatorcontrib>Ye, Ming</creatorcontrib><title>A method based on moving least squares for XRII image distortion correction</title><title>Medical physics (Lancaster)</title><addtitle>Med Phys</addtitle><description>This paper presents a novel integrated method to correct geometric distortions of XRII (x-ray image intensifier) images. The method has been compared, in terms of mean-squared residual error measured at control and intermediate points, with two traditional local methods and a traditional global methods. The proposed method is based on the methods of moving least squares (MLS) and polynomial fitting. Extensive experiments were performed on simulated and real XRII images. In simulation, the effect of pincushion distortion, sigmoidal distortion, local distortion, noise, and the number of control points was tested. The traditional local methods were sensitive to pincushion and sigmoidal distortion. The traditional global method was only sensitive to sigmoidal distortion. The proposed method was found neither sensitive to pincushion distortion nor sensitive to sigmoidal distortion. The sensitivity of the proposed method to local distortion was lower than or comparable with that of the traditional global method. The sensitivity of the proposed method to noise was higher than that of all three traditional methods. Nevertheless, provided the standard deviation of noise was not greater than 0.1 pixels, accuracy of the proposed method is still higher than the traditional methods. The sensitivity of the proposed method to the number of control points was greatly lower than that of the traditional methods. Provided that a proper cutoff radius is chosen, accuracy of the proposed method is higher than that of the traditional methods. Experiments on real images, carried out by using a 9 in. XRII, showed that residual error of the proposed method
(
0.2544
±
0.2479
pixels
)
is lower than that of the traditional global method
(
0.4223
±
0.3879
pixels
)
and local methods (
0.4555
±
0.3518
pixels
and
0.3696
±
0.4019
pixels
, respectively).</description><subject>aberrations</subject><subject>ACCURACY</subject><subject>Algorithms</subject><subject>BIOMEDICAL RADIOGRAPHY</subject><subject>Calibration</subject><subject>Computer Simulation</subject><subject>CORRECTIONS</subject><subject>diagnostic radiography</subject><subject>distortion correction</subject><subject>fluoroscopic image</subject><subject>Fluoroscopy - methods</subject><subject>General statistical methods</subject><subject>Humans</subject><subject>Image analysis</subject><subject>IMAGE INTENSIFIERS</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>least mean squares methods</subject><subject>LEAST SQUARE FIT</subject><subject>least squares</subject><subject>Least-Squares Analysis</subject><subject>Medical image distortion</subject><subject>Medical imaging</subject><subject>Medical X‐ray imaging</subject><subject>Models, Statistical</subject><subject>moving least squares</subject><subject>NOISE</subject><subject>polynomial approximation</subject><subject>POLYNOMIALS</subject><subject>Radiographic Image Interpretation, Computer-Assisted</subject><subject>RADIOLOGY AND NUCLEAR MEDICINE</subject><subject>Reproducibility of Results</subject><subject>SENSITIVITY</subject><subject>Sensitivity and Specificity</subject><subject>Signal generators</subject><subject>Software</subject><subject>X-RAY RADIOGRAPHY</subject><subject>X-Rays</subject><subject>XRII</subject><subject>X‐ and γ‐ray instruments</subject><subject>X‐ray imaging</subject><subject>X‐ray optics</subject><issn>0094-2405</issn><issn>2473-4209</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp90M1KxDAUBeAgio6jC19AAoKgUL1J00m6FPFnUFFEwV1Ik1QrbTMmnRHf3gwtKoiuEsiXw70HoR0CR4QQcUyOKM8JpHwFjSjjacIo5KtoBJCzhDLINtBmCK8AMEkzWEcbRACnTKQjdHWCG9u9OIMLFazBrsWNW1TtM66tCh0Ob3PlbcCl8_jpfjrFVaOeLTZV6Jzvqsi1897q5XULrZWqDnZ7OMfo8fzs4fQyub69mJ6eXCeakZwnZZoJxYvMZkZRrqkpOGG0yEBoYbXQE51NAIQQjBhRcCMI5ECKUuUG8vicjtFen-tCV8mgq87qF-3aNo4haeyBihSi2u_VzLu3uQ2dbKqgbV2r1rp5kJMcMsZJGuFBD7V3IXhbypmPW_oPSUAu-5VEDv1GuzuEzovGmm85FBpB0oP3qrYffyfJm7sh8LD3yzXUssWvPwvnf_iZKf_Dv0f9BKuKnNI</recordid><startdate>200711</startdate><enddate>200711</enddate><creator>Yan, Shiju</creator><creator>Wang, Chengtao</creator><creator>Ye, Ming</creator><general>American Association of Physicists in Medicine</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><scope>OTOTI</scope></search><sort><creationdate>200711</creationdate><title>A method based on moving least squares for XRII image distortion correction</title><author>Yan, Shiju ; Wang, Chengtao ; Ye, Ming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4197-f358a7b5e5da27c2db7142b508c8ec8c6c560088841d8b7d810901bfa9d098c63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>aberrations</topic><topic>ACCURACY</topic><topic>Algorithms</topic><topic>BIOMEDICAL RADIOGRAPHY</topic><topic>Calibration</topic><topic>Computer Simulation</topic><topic>CORRECTIONS</topic><topic>diagnostic radiography</topic><topic>distortion correction</topic><topic>fluoroscopic image</topic><topic>Fluoroscopy - methods</topic><topic>General statistical methods</topic><topic>Humans</topic><topic>Image analysis</topic><topic>IMAGE INTENSIFIERS</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>least mean squares methods</topic><topic>LEAST SQUARE FIT</topic><topic>least squares</topic><topic>Least-Squares Analysis</topic><topic>Medical image distortion</topic><topic>Medical imaging</topic><topic>Medical X‐ray imaging</topic><topic>Models, Statistical</topic><topic>moving least squares</topic><topic>NOISE</topic><topic>polynomial approximation</topic><topic>POLYNOMIALS</topic><topic>Radiographic Image Interpretation, Computer-Assisted</topic><topic>RADIOLOGY AND NUCLEAR MEDICINE</topic><topic>Reproducibility of Results</topic><topic>SENSITIVITY</topic><topic>Sensitivity and Specificity</topic><topic>Signal generators</topic><topic>Software</topic><topic>X-RAY RADIOGRAPHY</topic><topic>X-Rays</topic><topic>XRII</topic><topic>X‐ and γ‐ray instruments</topic><topic>X‐ray imaging</topic><topic>X‐ray optics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yan, Shiju</creatorcontrib><creatorcontrib>Wang, Chengtao</creatorcontrib><creatorcontrib>Ye, Ming</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><collection>OSTI.GOV</collection><jtitle>Medical physics (Lancaster)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yan, Shiju</au><au>Wang, Chengtao</au><au>Ye, Ming</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A method based on moving least squares for XRII image distortion correction</atitle><jtitle>Medical physics (Lancaster)</jtitle><addtitle>Med Phys</addtitle><date>2007-11</date><risdate>2007</risdate><volume>34</volume><issue>11</issue><spage>4194</spage><epage>4206</epage><pages>4194-4206</pages><issn>0094-2405</issn><eissn>2473-4209</eissn><coden>MPHYA6</coden><abstract>This paper presents a novel integrated method to correct geometric distortions of XRII (x-ray image intensifier) images. The method has been compared, in terms of mean-squared residual error measured at control and intermediate points, with two traditional local methods and a traditional global methods. The proposed method is based on the methods of moving least squares (MLS) and polynomial fitting. Extensive experiments were performed on simulated and real XRII images. In simulation, the effect of pincushion distortion, sigmoidal distortion, local distortion, noise, and the number of control points was tested. The traditional local methods were sensitive to pincushion and sigmoidal distortion. The traditional global method was only sensitive to sigmoidal distortion. The proposed method was found neither sensitive to pincushion distortion nor sensitive to sigmoidal distortion. The sensitivity of the proposed method to local distortion was lower than or comparable with that of the traditional global method. The sensitivity of the proposed method to noise was higher than that of all three traditional methods. Nevertheless, provided the standard deviation of noise was not greater than 0.1 pixels, accuracy of the proposed method is still higher than the traditional methods. The sensitivity of the proposed method to the number of control points was greatly lower than that of the traditional methods. Provided that a proper cutoff radius is chosen, accuracy of the proposed method is higher than that of the traditional methods. Experiments on real images, carried out by using a 9 in. XRII, showed that residual error of the proposed method
(
0.2544
±
0.2479
pixels
)
is lower than that of the traditional global method
(
0.4223
±
0.3879
pixels
)
and local methods (
0.4555
±
0.3518
pixels
and
0.3696
±
0.4019
pixels
, respectively).</abstract><cop>United States</cop><pub>American Association of Physicists in Medicine</pub><pmid>18072483</pmid><doi>10.1118/1.2791037</doi><tpages>13</tpages></addata></record> |
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source | MEDLINE; Access via Wiley Online Library |
subjects | aberrations ACCURACY Algorithms BIOMEDICAL RADIOGRAPHY Calibration Computer Simulation CORRECTIONS diagnostic radiography distortion correction fluoroscopic image Fluoroscopy - methods General statistical methods Humans Image analysis IMAGE INTENSIFIERS Image Processing, Computer-Assisted - methods least mean squares methods LEAST SQUARE FIT least squares Least-Squares Analysis Medical image distortion Medical imaging Medical X‐ray imaging Models, Statistical moving least squares NOISE polynomial approximation POLYNOMIALS Radiographic Image Interpretation, Computer-Assisted RADIOLOGY AND NUCLEAR MEDICINE Reproducibility of Results SENSITIVITY Sensitivity and Specificity Signal generators Software X-RAY RADIOGRAPHY X-Rays XRII X‐ and γ‐ray instruments X‐ray imaging X‐ray optics |
title | A method based on moving least squares for XRII image distortion correction |
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