Intensity inhomogeneity correction for the breast sonogram: Constrained fuzzy cell-based bipartitioning and polynomial surface modeling
Purpose: To develop an intensity inhomogeneity algorithm for breast sonograms in order to assist visual identification and automatic delineation of lesion boundaries. Methods: The proposed algorithm was composed of two essential ideas. One was decomposing the region of interest (ROI) into foreground...
Gespeichert in:
| Veröffentlicht in: | Medical physics (Lancaster) 2010-11, Vol.37 (11), p.5645-5654 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 5654 |
|---|---|
| container_issue | 11 |
| container_start_page | 5645 |
| container_title | Medical physics (Lancaster) |
| container_volume | 37 |
| creator | Lee, Chia-Yen Chou, Yi-Hong Huang, Chiun-Sheng Chang, Yeun-Chung Tiu, Chui-Mei Chen, Chung-Ming |
| description | Purpose:
To develop an intensity inhomogeneity algorithm for breast sonograms in order to assist visual identification and automatic delineation of lesion boundaries.
Methods:
The proposed algorithm was composed of two essential ideas. One was decomposing the region of interest (ROI) into foreground and background regions by a cell-based segmentation algorithm, called constrained fuzzy cell-based bipartition-EM (CFCB-EM) algorithm. The CFCB-EM algorithm deformed the contour in a fuzzy cell-based deformation fashion with the cell structures derived by the fuzzy cell competition (FCC) algorithm as the deformation unit and the boundary estimated by the normalized cut (NC) algorithm as the reference contour. The other was modeling the intensity inhomogeneity in an ROI as a spatially variant normal distribution with a constant variance and spatially variant means, which formed a polynomial surface of order
n
. The proposed algorithm was formulated as a nested EM algorithm comprising the outer-layer EM algorithm, i.e., the intensity inhomogeneity correction-EM (IIC-EM) algorithm, and the inner-layer EM algorithm, i.e., the CFCB-EM algorithm. The E step of the IIC-EM algorithm was to provide a reasonably good bipartition separating the ROI into foreground and background regions, which included three major component algorithms, namely, the FCC, the NC, and the CFCB-EM. The M step of the IIC-EM algorithm was to estimate and correct the intensity inhomogeneity field by least-squared fitting the intensity inhomogeneity to an
n
th
order polynomial surface. Forty-nine breast sonograms with intensity inhomogeneity, each from a different subject, were randomly selected for performance analysis. Three assessments were carried out to evaluate the effectiveness of the proposed algorithm.
Results:
Based on the visual evaluation of two experienced radiologists, in the first assessment, 46 out of 49 breast lesions were considered to have better contrasts on the inhomogeneity-corrected images by both radiologists. The interrater reliability for the radiologists was found to be
kappa
=
0.479
(
p
=
0.001
)
. In the second assessment, the mean gradients of the low-gradient boundary points before and after correction of the intensity inhomogeneity were compared by the paired t-test, yielding a
p
-value of 0.000, which suggested the proposed intensity inhomogeneity algorithm may enhance the mean gradient of the low-gradient boundary points. By using the paired t-test, the third asse |
| doi_str_mv | 10.1118/1.3488944 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmed_primary_21158276</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>818640800</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4474-856fce1b2a4db1aeb37bb2a7acb81ad5cd1b278b3c0cd5f68d562a76705107df3</originalsourceid><addsrcrecordid>eNqNkc9u1DAQh60K1C6lh74A8g1RKa2dOImXQ6VqVaBSUTnA2fKfydYosYPtFG1foK-Nwy6Fy0qcrPF8_sb6DUKnlJxTSvkFPa8Y50vGDtCiZG1VsJIsX6AFIUtWlIzUR-hVjN8JIU1Vk0N0VFJa87JtFujpxiVw0aYNtu7eD34NDuZK-xBAJ-sd7nzA6R6wCiBjwtE7vw5yeI9X3sUUpHVgcDc9PuZX0PeFkjFfKDvKkOxssG6NpTN49P3G-cHKHscpdFIDHryBPvdfo5ed7COc7M5j9O3D9dfVp-L27uPN6uq20Iy1rOB102mgqpTMKCpBVa3KRSu14lSaWpvca7mqNNGm7hpu6ia3m5bUlLSmq47R2613DP7HBDGJwcb519KBn6LglDeMcEIy-W5L6uBjDNCJMdhBho2gRMyxCyp2sWf2zc46qQHMM_kn5wwUW-Cn7WGz3yQ-f9kJL7d81DbJOcP9b543KP5uMAvO9gkefPhn4Pg7lLP_nlb9AgEEwQk</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>818640800</pqid></control><display><type>article</type><title>Intensity inhomogeneity correction for the breast sonogram: Constrained fuzzy cell-based bipartitioning and polynomial surface modeling</title><source>MEDLINE</source><source>Access via Wiley Online Library</source><source>Alma/SFX Local Collection</source><creator>Lee, Chia-Yen ; Chou, Yi-Hong ; Huang, Chiun-Sheng ; Chang, Yeun-Chung ; Tiu, Chui-Mei ; Chen, Chung-Ming</creator><creatorcontrib>Lee, Chia-Yen ; Chou, Yi-Hong ; Huang, Chiun-Sheng ; Chang, Yeun-Chung ; Tiu, Chui-Mei ; Chen, Chung-Ming</creatorcontrib><description>Purpose:
To develop an intensity inhomogeneity algorithm for breast sonograms in order to assist visual identification and automatic delineation of lesion boundaries.
Methods:
The proposed algorithm was composed of two essential ideas. One was decomposing the region of interest (ROI) into foreground and background regions by a cell-based segmentation algorithm, called constrained fuzzy cell-based bipartition-EM (CFCB-EM) algorithm. The CFCB-EM algorithm deformed the contour in a fuzzy cell-based deformation fashion with the cell structures derived by the fuzzy cell competition (FCC) algorithm as the deformation unit and the boundary estimated by the normalized cut (NC) algorithm as the reference contour. The other was modeling the intensity inhomogeneity in an ROI as a spatially variant normal distribution with a constant variance and spatially variant means, which formed a polynomial surface of order
n
. The proposed algorithm was formulated as a nested EM algorithm comprising the outer-layer EM algorithm, i.e., the intensity inhomogeneity correction-EM (IIC-EM) algorithm, and the inner-layer EM algorithm, i.e., the CFCB-EM algorithm. The E step of the IIC-EM algorithm was to provide a reasonably good bipartition separating the ROI into foreground and background regions, which included three major component algorithms, namely, the FCC, the NC, and the CFCB-EM. The M step of the IIC-EM algorithm was to estimate and correct the intensity inhomogeneity field by least-squared fitting the intensity inhomogeneity to an
n
th
order polynomial surface. Forty-nine breast sonograms with intensity inhomogeneity, each from a different subject, were randomly selected for performance analysis. Three assessments were carried out to evaluate the effectiveness of the proposed algorithm.
Results:
Based on the visual evaluation of two experienced radiologists, in the first assessment, 46 out of 49 breast lesions were considered to have better contrasts on the inhomogeneity-corrected images by both radiologists. The interrater reliability for the radiologists was found to be
kappa
=
0.479
(
p
=
0.001
)
. In the second assessment, the mean gradients of the low-gradient boundary points before and after correction of the intensity inhomogeneity were compared by the paired t-test, yielding a
p
-value of 0.000, which suggested the proposed intensity inhomogeneity algorithm may enhance the mean gradient of the low-gradient boundary points. By using the paired t-test, the third assessment further showed that the Chan and Vese level set method could derive a much better lesion boundary on the inhomogeneity-corrected image than on the original image
(
p
=
0.000
)
.
Conclusions:
The proposed intensity inhomogeneity correction algorithm could not only augment the visibility of lesion boundary but also improve the segmentation result on a breast sonogram.</description><identifier>ISSN: 0094-2405</identifier><identifier>EISSN: 2473-4209</identifier><identifier>DOI: 10.1118/1.3488944</identifier><identifier>PMID: 21158276</identifier><identifier>CODEN: MPHYA6</identifier><language>eng</language><publisher>United States: American Association of Physicists in Medicine</publisher><subject>Acoustical medical instrumentation and measurement techniques ; Algorithms ; Automation ; biomedical ultrasonics ; Breast Neoplasms - diagnosis ; Breast Neoplasms - pathology ; breast sonogram ; Eigenvalues ; expectation‐maximisation algorithm ; Female ; fuzzy cell competition ; Fuzzy Logic ; Humans ; Image Processing, Computer-Assisted - methods ; image segmentation ; intensity inhomogeneity correction ; Iteration theory ; mammography ; Medical image contrast ; medical image processing ; Medical image segmentation ; Medical imaging ; Models, Statistical ; Models, Theoretical ; Observer Variation ; polynomial approximation ; polynomial surface ; Polynomials ; Radiologists ; Segmentation ; Software ; Spatial analysis ; Ultrasonics ; Ultrasonography ; Ultrasonography - methods</subject><ispartof>Medical physics (Lancaster), 2010-11, Vol.37 (11), p.5645-5654</ispartof><rights>American Association of Physicists in Medicine</rights><rights>2010 American Association of Physicists in Medicine</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4474-856fce1b2a4db1aeb37bb2a7acb81ad5cd1b278b3c0cd5f68d562a76705107df3</citedby><cites>FETCH-LOGICAL-c4474-856fce1b2a4db1aeb37bb2a7acb81ad5cd1b278b3c0cd5f68d562a76705107df3</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%2F1.3488944$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1118%2F1.3488944$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21158276$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lee, Chia-Yen</creatorcontrib><creatorcontrib>Chou, Yi-Hong</creatorcontrib><creatorcontrib>Huang, Chiun-Sheng</creatorcontrib><creatorcontrib>Chang, Yeun-Chung</creatorcontrib><creatorcontrib>Tiu, Chui-Mei</creatorcontrib><creatorcontrib>Chen, Chung-Ming</creatorcontrib><title>Intensity inhomogeneity correction for the breast sonogram: Constrained fuzzy cell-based bipartitioning and polynomial surface modeling</title><title>Medical physics (Lancaster)</title><addtitle>Med Phys</addtitle><description>Purpose:
To develop an intensity inhomogeneity algorithm for breast sonograms in order to assist visual identification and automatic delineation of lesion boundaries.
Methods:
The proposed algorithm was composed of two essential ideas. One was decomposing the region of interest (ROI) into foreground and background regions by a cell-based segmentation algorithm, called constrained fuzzy cell-based bipartition-EM (CFCB-EM) algorithm. The CFCB-EM algorithm deformed the contour in a fuzzy cell-based deformation fashion with the cell structures derived by the fuzzy cell competition (FCC) algorithm as the deformation unit and the boundary estimated by the normalized cut (NC) algorithm as the reference contour. The other was modeling the intensity inhomogeneity in an ROI as a spatially variant normal distribution with a constant variance and spatially variant means, which formed a polynomial surface of order
n
. The proposed algorithm was formulated as a nested EM algorithm comprising the outer-layer EM algorithm, i.e., the intensity inhomogeneity correction-EM (IIC-EM) algorithm, and the inner-layer EM algorithm, i.e., the CFCB-EM algorithm. The E step of the IIC-EM algorithm was to provide a reasonably good bipartition separating the ROI into foreground and background regions, which included three major component algorithms, namely, the FCC, the NC, and the CFCB-EM. The M step of the IIC-EM algorithm was to estimate and correct the intensity inhomogeneity field by least-squared fitting the intensity inhomogeneity to an
n
th
order polynomial surface. Forty-nine breast sonograms with intensity inhomogeneity, each from a different subject, were randomly selected for performance analysis. Three assessments were carried out to evaluate the effectiveness of the proposed algorithm.
Results:
Based on the visual evaluation of two experienced radiologists, in the first assessment, 46 out of 49 breast lesions were considered to have better contrasts on the inhomogeneity-corrected images by both radiologists. The interrater reliability for the radiologists was found to be
kappa
=
0.479
(
p
=
0.001
)
. In the second assessment, the mean gradients of the low-gradient boundary points before and after correction of the intensity inhomogeneity were compared by the paired t-test, yielding a
p
-value of 0.000, which suggested the proposed intensity inhomogeneity algorithm may enhance the mean gradient of the low-gradient boundary points. By using the paired t-test, the third assessment further showed that the Chan and Vese level set method could derive a much better lesion boundary on the inhomogeneity-corrected image than on the original image
(
p
=
0.000
)
.
Conclusions:
The proposed intensity inhomogeneity correction algorithm could not only augment the visibility of lesion boundary but also improve the segmentation result on a breast sonogram.</description><subject>Acoustical medical instrumentation and measurement techniques</subject><subject>Algorithms</subject><subject>Automation</subject><subject>biomedical ultrasonics</subject><subject>Breast Neoplasms - diagnosis</subject><subject>Breast Neoplasms - pathology</subject><subject>breast sonogram</subject><subject>Eigenvalues</subject><subject>expectation‐maximisation algorithm</subject><subject>Female</subject><subject>fuzzy cell competition</subject><subject>Fuzzy Logic</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>image segmentation</subject><subject>intensity inhomogeneity correction</subject><subject>Iteration theory</subject><subject>mammography</subject><subject>Medical image contrast</subject><subject>medical image processing</subject><subject>Medical image segmentation</subject><subject>Medical imaging</subject><subject>Models, Statistical</subject><subject>Models, Theoretical</subject><subject>Observer Variation</subject><subject>polynomial approximation</subject><subject>polynomial surface</subject><subject>Polynomials</subject><subject>Radiologists</subject><subject>Segmentation</subject><subject>Software</subject><subject>Spatial analysis</subject><subject>Ultrasonics</subject><subject>Ultrasonography</subject><subject>Ultrasonography - methods</subject><issn>0094-2405</issn><issn>2473-4209</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkc9u1DAQh60K1C6lh74A8g1RKa2dOImXQ6VqVaBSUTnA2fKfydYosYPtFG1foK-Nwy6Fy0qcrPF8_sb6DUKnlJxTSvkFPa8Y50vGDtCiZG1VsJIsX6AFIUtWlIzUR-hVjN8JIU1Vk0N0VFJa87JtFujpxiVw0aYNtu7eD34NDuZK-xBAJ-sd7nzA6R6wCiBjwtE7vw5yeI9X3sUUpHVgcDc9PuZX0PeFkjFfKDvKkOxssG6NpTN49P3G-cHKHscpdFIDHryBPvdfo5ed7COc7M5j9O3D9dfVp-L27uPN6uq20Iy1rOB102mgqpTMKCpBVa3KRSu14lSaWpvca7mqNNGm7hpu6ia3m5bUlLSmq47R2613DP7HBDGJwcb519KBn6LglDeMcEIy-W5L6uBjDNCJMdhBho2gRMyxCyp2sWf2zc46qQHMM_kn5wwUW-Cn7WGz3yQ-f9kJL7d81DbJOcP9b543KP5uMAvO9gkefPhn4Pg7lLP_nlb9AgEEwQk</recordid><startdate>201011</startdate><enddate>201011</enddate><creator>Lee, Chia-Yen</creator><creator>Chou, Yi-Hong</creator><creator>Huang, Chiun-Sheng</creator><creator>Chang, Yeun-Chung</creator><creator>Tiu, Chui-Mei</creator><creator>Chen, Chung-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></search><sort><creationdate>201011</creationdate><title>Intensity inhomogeneity correction for the breast sonogram: Constrained fuzzy cell-based bipartitioning and polynomial surface modeling</title><author>Lee, Chia-Yen ; Chou, Yi-Hong ; Huang, Chiun-Sheng ; Chang, Yeun-Chung ; Tiu, Chui-Mei ; Chen, Chung-Ming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4474-856fce1b2a4db1aeb37bb2a7acb81ad5cd1b278b3c0cd5f68d562a76705107df3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Acoustical medical instrumentation and measurement techniques</topic><topic>Algorithms</topic><topic>Automation</topic><topic>biomedical ultrasonics</topic><topic>Breast Neoplasms - diagnosis</topic><topic>Breast Neoplasms - pathology</topic><topic>breast sonogram</topic><topic>Eigenvalues</topic><topic>expectation‐maximisation algorithm</topic><topic>Female</topic><topic>fuzzy cell competition</topic><topic>Fuzzy Logic</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>image segmentation</topic><topic>intensity inhomogeneity correction</topic><topic>Iteration theory</topic><topic>mammography</topic><topic>Medical image contrast</topic><topic>medical image processing</topic><topic>Medical image segmentation</topic><topic>Medical imaging</topic><topic>Models, Statistical</topic><topic>Models, Theoretical</topic><topic>Observer Variation</topic><topic>polynomial approximation</topic><topic>polynomial surface</topic><topic>Polynomials</topic><topic>Radiologists</topic><topic>Segmentation</topic><topic>Software</topic><topic>Spatial analysis</topic><topic>Ultrasonics</topic><topic>Ultrasonography</topic><topic>Ultrasonography - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, Chia-Yen</creatorcontrib><creatorcontrib>Chou, Yi-Hong</creatorcontrib><creatorcontrib>Huang, Chiun-Sheng</creatorcontrib><creatorcontrib>Chang, Yeun-Chung</creatorcontrib><creatorcontrib>Tiu, Chui-Mei</creatorcontrib><creatorcontrib>Chen, Chung-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><jtitle>Medical physics (Lancaster)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lee, Chia-Yen</au><au>Chou, Yi-Hong</au><au>Huang, Chiun-Sheng</au><au>Chang, Yeun-Chung</au><au>Tiu, Chui-Mei</au><au>Chen, Chung-Ming</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Intensity inhomogeneity correction for the breast sonogram: Constrained fuzzy cell-based bipartitioning and polynomial surface modeling</atitle><jtitle>Medical physics (Lancaster)</jtitle><addtitle>Med Phys</addtitle><date>2010-11</date><risdate>2010</risdate><volume>37</volume><issue>11</issue><spage>5645</spage><epage>5654</epage><pages>5645-5654</pages><issn>0094-2405</issn><eissn>2473-4209</eissn><coden>MPHYA6</coden><abstract>Purpose:
To develop an intensity inhomogeneity algorithm for breast sonograms in order to assist visual identification and automatic delineation of lesion boundaries.
Methods:
The proposed algorithm was composed of two essential ideas. One was decomposing the region of interest (ROI) into foreground and background regions by a cell-based segmentation algorithm, called constrained fuzzy cell-based bipartition-EM (CFCB-EM) algorithm. The CFCB-EM algorithm deformed the contour in a fuzzy cell-based deformation fashion with the cell structures derived by the fuzzy cell competition (FCC) algorithm as the deformation unit and the boundary estimated by the normalized cut (NC) algorithm as the reference contour. The other was modeling the intensity inhomogeneity in an ROI as a spatially variant normal distribution with a constant variance and spatially variant means, which formed a polynomial surface of order
n
. The proposed algorithm was formulated as a nested EM algorithm comprising the outer-layer EM algorithm, i.e., the intensity inhomogeneity correction-EM (IIC-EM) algorithm, and the inner-layer EM algorithm, i.e., the CFCB-EM algorithm. The E step of the IIC-EM algorithm was to provide a reasonably good bipartition separating the ROI into foreground and background regions, which included three major component algorithms, namely, the FCC, the NC, and the CFCB-EM. The M step of the IIC-EM algorithm was to estimate and correct the intensity inhomogeneity field by least-squared fitting the intensity inhomogeneity to an
n
th
order polynomial surface. Forty-nine breast sonograms with intensity inhomogeneity, each from a different subject, were randomly selected for performance analysis. Three assessments were carried out to evaluate the effectiveness of the proposed algorithm.
Results:
Based on the visual evaluation of two experienced radiologists, in the first assessment, 46 out of 49 breast lesions were considered to have better contrasts on the inhomogeneity-corrected images by both radiologists. The interrater reliability for the radiologists was found to be
kappa
=
0.479
(
p
=
0.001
)
. In the second assessment, the mean gradients of the low-gradient boundary points before and after correction of the intensity inhomogeneity were compared by the paired t-test, yielding a
p
-value of 0.000, which suggested the proposed intensity inhomogeneity algorithm may enhance the mean gradient of the low-gradient boundary points. By using the paired t-test, the third assessment further showed that the Chan and Vese level set method could derive a much better lesion boundary on the inhomogeneity-corrected image than on the original image
(
p
=
0.000
)
.
Conclusions:
The proposed intensity inhomogeneity correction algorithm could not only augment the visibility of lesion boundary but also improve the segmentation result on a breast sonogram.</abstract><cop>United States</cop><pub>American Association of Physicists in Medicine</pub><pmid>21158276</pmid><doi>10.1118/1.3488944</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0094-2405 |
| ispartof | Medical physics (Lancaster), 2010-11, Vol.37 (11), p.5645-5654 |
| issn | 0094-2405 2473-4209 |
| language | eng |
| recordid | cdi_pubmed_primary_21158276 |
| source | MEDLINE; Access via Wiley Online Library; Alma/SFX Local Collection |
| subjects | Acoustical medical instrumentation and measurement techniques Algorithms Automation biomedical ultrasonics Breast Neoplasms - diagnosis Breast Neoplasms - pathology breast sonogram Eigenvalues expectation‐maximisation algorithm Female fuzzy cell competition Fuzzy Logic Humans Image Processing, Computer-Assisted - methods image segmentation intensity inhomogeneity correction Iteration theory mammography Medical image contrast medical image processing Medical image segmentation Medical imaging Models, Statistical Models, Theoretical Observer Variation polynomial approximation polynomial surface Polynomials Radiologists Segmentation Software Spatial analysis Ultrasonics Ultrasonography Ultrasonography - methods |
| title | Intensity inhomogeneity correction for the breast sonogram: Constrained fuzzy cell-based bipartitioning and polynomial surface modeling |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T02%3A50%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Intensity%20inhomogeneity%20correction%20for%20the%20breast%20sonogram:%20Constrained%20fuzzy%20cell-based%20bipartitioning%20and%20polynomial%20surface%20modeling&rft.jtitle=Medical%20physics%20(Lancaster)&rft.au=Lee,%20Chia-Yen&rft.date=2010-11&rft.volume=37&rft.issue=11&rft.spage=5645&rft.epage=5654&rft.pages=5645-5654&rft.issn=0094-2405&rft.eissn=2473-4209&rft.coden=MPHYA6&rft_id=info:doi/10.1118/1.3488944&rft_dat=%3Cproquest_pubme%3E818640800%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=818640800&rft_id=info:pmid/21158276&rfr_iscdi=true |