Image contrast enhancement based on a histogram transformation of local standard deviation
The adaptive contrast enhancement (ACE) algorithm, which uses contrast gains (CGs) to adjust the high-frequency components of images, is a well-known technique for medical image processing. Conventionally, the CG is either a constant or inversely proportional to the local standard deviation (LSD). H...
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| Veröffentlicht in: | IEEE transactions on medical imaging 1998-08, Vol.17 (4), p.518-531 |
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| creator | CHANG, D.-C WU, W.-R |
| description | The adaptive contrast enhancement (ACE) algorithm, which uses contrast gains (CGs) to adjust the high-frequency components of images, is a well-known technique for medical image processing. Conventionally, the CG is either a constant or inversely proportional to the local standard deviation (LSD). However, it is known that conventional approaches entail noise overenhancement and ringing artifacts. In this paper, the authors present a new ACE algorithm that eliminates these problems. First, a mathematical model for the LSD distribution is proposed by extending Hunt's (1976) image model. Then, the CG is formulated as a function of the LSD. The function, which is nonlinear, is determined by the transformation between the LSD histogram and a desired LSD distribution. Using the authors' formulation, it can be shown that conventional ACEs use linear functions to compute the new CGs. It is the proposed nonlinear function that produces an adequate CG resulting in little noise overenhancement and fewer ringing artifacts. Finally, simulations using some X-ray images are provided to demonstrate the effectiveness of the the authors' new algorithm. |
| doi_str_mv | 10.1109/42.730397 |
| format | Article |
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Conventionally, the CG is either a constant or inversely proportional to the local standard deviation (LSD). However, it is known that conventional approaches entail noise overenhancement and ringing artifacts. In this paper, the authors present a new ACE algorithm that eliminates these problems. First, a mathematical model for the LSD distribution is proposed by extending Hunt's (1976) image model. Then, the CG is formulated as a function of the LSD. The function, which is nonlinear, is determined by the transformation between the LSD histogram and a desired LSD distribution. Using the authors' formulation, it can be shown that conventional ACEs use linear functions to compute the new CGs. It is the proposed nonlinear function that produces an adequate CG resulting in little noise overenhancement and fewer ringing artifacts. 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Nmr spectrometry ; Radiographic Image Enhancement - methods ; Radiography, Thoracic - methods ; Statistical methods ; X ray radiography ; X-ray imaging</subject><ispartof>IEEE transactions on medical imaging, 1998-08, Vol.17 (4), p.518-531</ispartof><rights>1999 INIST-CNRS</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c390t-18b0156bc382bfb9137f3f937e096e4d34170e02ea72bd0db55af0e10ee541d3</citedby><cites>FETCH-LOGICAL-c390t-18b0156bc382bfb9137f3f937e096e4d34170e02ea72bd0db55af0e10ee541d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/730397$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/730397$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=1620317$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/9845308$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>CHANG, D.-C</creatorcontrib><creatorcontrib>WU, W.-R</creatorcontrib><title>Image contrast enhancement based on a histogram transformation of local standard deviation</title><title>IEEE transactions on medical imaging</title><addtitle>TMI</addtitle><addtitle>IEEE Trans Med Imaging</addtitle><description>The adaptive contrast enhancement (ACE) algorithm, which uses contrast gains (CGs) to adjust the high-frequency components of images, is a well-known technique for medical image processing. Conventionally, the CG is either a constant or inversely proportional to the local standard deviation (LSD). However, it is known that conventional approaches entail noise overenhancement and ringing artifacts. In this paper, the authors present a new ACE algorithm that eliminates these problems. First, a mathematical model for the LSD distribution is proposed by extending Hunt's (1976) image model. Then, the CG is formulated as a function of the LSD. The function, which is nonlinear, is determined by the transformation between the LSD histogram and a desired LSD distribution. Using the authors' formulation, it can be shown that conventional ACEs use linear functions to compute the new CGs. It is the proposed nonlinear function that produces an adequate CG resulting in little noise overenhancement and fewer ringing artifacts. Finally, simulations using some X-ray images are provided to demonstrate the effectiveness of the the authors' new algorithm.</description><subject>Algorithms</subject><subject>Attenuation</subject><subject>Biological and medical sciences</subject><subject>Biomedical image processing</subject><subject>Biomedical imaging</subject><subject>Character generation</subject><subject>Computational modeling</subject><subject>Computerized, statistical medical data processing and models in biomedicine</subject><subject>Diagnostic radiography</subject><subject>General aspects. Methods</subject><subject>Histograms</subject><subject>Humans</subject><subject>Image enhancement</subject><subject>Investigative techniques, diagnostic techniques (general aspects)</subject><subject>Mathematical model</subject><subject>Mathematical models</subject><subject>Mathematical transformations</subject><subject>Medical diagnostic imaging</subject><subject>Medical sciences</subject><subject>Miscellaneous. Technology</subject><subject>Radiodiagnosis. Nmr imagery. Nmr spectrometry</subject><subject>Radiographic Image Enhancement - methods</subject><subject>Radiography, Thoracic - methods</subject><subject>Statistical methods</subject><subject>X ray radiography</subject><subject>X-ray imaging</subject><issn>0278-0062</issn><issn>1558-254X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1998</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqNkcFL5DAUxoPsMo6jB68LQg4i7KH6XtI0zVFEXUHYyxyWvZS0eRm7tI2bdIT97-04RY_uJe_w-_EFvo-xU4RLRDBXubjUEqTRB2yJSpWZUPmvL2wJQpcZQCEO2VFKfwAwV2AWbGHKXEkol-z3Q283xJswjNGmkdPwZIeGehpGXttEjoeBW_7UpjFsou35pA3Jh9jbsZ1Q8LwLje14Gu3gbHTc0Uv7xo7ZV2-7RCfzXbH13e365kf2-PP-4eb6MWukgTHDsgZURd3IUtS-Nii1l95ITWAKyp3MUQOBIKtF7cDVSlkPhECkcnRyxS72sc8x_N1SGqu-TQ11nR0obFOlAQGNgk9FUUpdFOV_iDg1jdO7Yt_3YhNDSpF89Rzb3sZ_FUK1G6bKRbUfZnLP5tBt3ZN7N-clJn4-c5umPv1Uc9Omj8BCgMRdzLe91hLRO53_eAXWKp0a</recordid><startdate>19980801</startdate><enddate>19980801</enddate><creator>CHANG, D.-C</creator><creator>WU, W.-R</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><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>7SC</scope><scope>7U5</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope></search><sort><creationdate>19980801</creationdate><title>Image contrast enhancement based on a histogram transformation of local standard deviation</title><author>CHANG, D.-C ; WU, W.-R</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c390t-18b0156bc382bfb9137f3f937e096e4d34170e02ea72bd0db55af0e10ee541d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Algorithms</topic><topic>Attenuation</topic><topic>Biological and medical sciences</topic><topic>Biomedical image processing</topic><topic>Biomedical imaging</topic><topic>Character generation</topic><topic>Computational modeling</topic><topic>Computerized, statistical medical data processing and models in biomedicine</topic><topic>Diagnostic radiography</topic><topic>General aspects. Methods</topic><topic>Histograms</topic><topic>Humans</topic><topic>Image enhancement</topic><topic>Investigative techniques, diagnostic techniques (general aspects)</topic><topic>Mathematical model</topic><topic>Mathematical models</topic><topic>Mathematical transformations</topic><topic>Medical diagnostic imaging</topic><topic>Medical sciences</topic><topic>Miscellaneous. Technology</topic><topic>Radiodiagnosis. Nmr imagery. Nmr spectrometry</topic><topic>Radiographic Image Enhancement - methods</topic><topic>Radiography, Thoracic - methods</topic><topic>Statistical methods</topic><topic>X ray radiography</topic><topic>X-ray imaging</topic><toplevel>online_resources</toplevel><creatorcontrib>CHANG, D.-C</creatorcontrib><creatorcontrib>WU, W.-R</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on medical imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>CHANG, D.-C</au><au>WU, W.-R</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Image contrast enhancement based on a histogram transformation of local standard deviation</atitle><jtitle>IEEE transactions on medical imaging</jtitle><stitle>TMI</stitle><addtitle>IEEE Trans Med Imaging</addtitle><date>1998-08-01</date><risdate>1998</risdate><volume>17</volume><issue>4</issue><spage>518</spage><epage>531</epage><pages>518-531</pages><issn>0278-0062</issn><eissn>1558-254X</eissn><coden>ITMID4</coden><abstract>The adaptive contrast enhancement (ACE) algorithm, which uses contrast gains (CGs) to adjust the high-frequency components of images, is a well-known technique for medical image processing. Conventionally, the CG is either a constant or inversely proportional to the local standard deviation (LSD). However, it is known that conventional approaches entail noise overenhancement and ringing artifacts. In this paper, the authors present a new ACE algorithm that eliminates these problems. First, a mathematical model for the LSD distribution is proposed by extending Hunt's (1976) image model. Then, the CG is formulated as a function of the LSD. The function, which is nonlinear, is determined by the transformation between the LSD histogram and a desired LSD distribution. Using the authors' formulation, it can be shown that conventional ACEs use linear functions to compute the new CGs. It is the proposed nonlinear function that produces an adequate CG resulting in little noise overenhancement and fewer ringing artifacts. 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| subjects | Algorithms Attenuation Biological and medical sciences Biomedical image processing Biomedical imaging Character generation Computational modeling Computerized, statistical medical data processing and models in biomedicine Diagnostic radiography General aspects. Methods Histograms Humans Image enhancement Investigative techniques, diagnostic techniques (general aspects) Mathematical model Mathematical models Mathematical transformations Medical diagnostic imaging Medical sciences Miscellaneous. Technology Radiodiagnosis. Nmr imagery. Nmr spectrometry Radiographic Image Enhancement - methods Radiography, Thoracic - methods Statistical methods X ray radiography X-ray imaging |
| title | Image contrast enhancement based on a histogram transformation of local standard deviation |
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