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...
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| Published in | Medical physics (Lancaster) Vol. 37; no. 11; pp. 5645 - 5654 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
American Association of Physicists in Medicine
01.11.2010
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0094-2405 2473-4209 |
| DOI | 10.1118/1.3488944 |
Cover
| 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. |
|---|---|
| AbstractList | To develop an intensity inhomogeneity algorithm for breast sonograms in order to assist visual identification and automatic delineation of lesion boundaries.
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 nth 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.
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).
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. 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 ordern. 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 nth 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 bekappa=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. 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. To develop an intensity inhomogeneity algorithm for breast sonograms in order to assist visual identification and automatic delineation of lesion boundaries.PURPOSETo develop an intensity inhomogeneity algorithm for breast sonograms in order to assist visual identification and automatic delineation of lesion boundaries.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 nth 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.METHODSThe 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 nth 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.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).RESULTSBased 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).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.CONCLUSIONSThe proposed intensity inhomogeneity correction algorithm could not only augment the visibility of lesion boundary but also improve the segmentation result on a breast sonogram. |
| Author | Tiu, Chui-Mei Chen, Chung-Ming Chou, Yi-Hong Huang, Chiun-Sheng Chang, Yeun-Chung Lee, Chia-Yen |
| Author_xml | – sequence: 1 givenname: Chia-Yen surname: Lee fullname: Lee, Chia-Yen organization: Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Number. 1, Section 1, Jen-Ai Road, Taipei 100, Taiwan – sequence: 2 givenname: Yi-Hong surname: Chou fullname: Chou, Yi-Hong organization: Department of Radiology, Taipei Veterans General Hospital and National Yang Ming University, Taipei 112, Taiwan – sequence: 3 givenname: Chiun-Sheng surname: Huang fullname: Huang, Chiun-Sheng organization: Department of Surgery, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei 100, Taiwan – sequence: 4 givenname: Yeun-Chung surname: Chang fullname: Chang, Yeun-Chung organization: Department of Medical Imaging, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei 100, Taiwan – sequence: 5 givenname: Chui-Mei surname: Tiu fullname: Tiu, Chui-Mei organization: Department of Radiology, Taipei Veterans General Hospital and National Yang Ming University, Taipei 112, Taiwan – sequence: 6 givenname: Chung-Ming surname: Chen fullname: Chen, Chung-Ming email: chung@ntu.edu.tw organization: Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Number. 1, Section 1, Jen-Ai Road, Taipei 100, Taiwan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21158276$$D View this record in MEDLINE/PubMed |
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| Keywords | segmentation fuzzy cell competition polynomial surface intensity inhomogeneity correction breast sonogram |
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| Notes | Author to whom correspondence should be addressed. Electronic mail Telephone: +886‐2‐33665273; Fax: +886‐2‐33665268. chung@ntu.edu.tw 0094‐2405/2010/37(11)/5645/10/$30.00 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
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| References | Chang, Wu, Moon, Chen (c21) 2005; 89 Dawant, Zijdenbos, Margolin (c12) 1993; 12 Zhang, Brady, Smith (c14) 2001; 20 Chen (c15) 2005; 31 O’Donnell (c1) 1983; SU-30 Sled, Zijdenbos, Evans (c8) 1998; 17 Sahiner (c19) 2007; 242 Chan, Vese (c23) 2001; 10 Vovk, Pernuš, Likar (c9) 2004; 49 Jianbo, Jitendra (c16) 2000; 22 Xiao, Brady, Noble, Zhang (c4) 2002; 21 Pye, Wild, McDicken (c2) 1992; 18 Chen, Chang, Wu, Moon, Wu (c20) 2003; 29 Horsch, Giger, Venta, Vyborny (c18) 2002; 29 Hughes, Duck (c3) 1997; 23 Vovk, Pernuš, Likar (c5) 2007; 26 Ashburner, Friston (c11) 2005; 26 Lai, Fang (c13) 1999; 3 Joo, Yang, Moon, Kim (c17) 2004; 23 Brinkmann, Manduca, Robb (c6) 1998; 17 Lewis, Fox (c7) 2004; 23 Ahmed, Yamany, Mohamed, Farag, Moriarty (c10) 2002; 21 Chan, T.; Vese, L. 2001; 10 Lai, S.; Fang, M. 1999; 3 Sahiner, B. 2007; 242 Hughes, D.; Duck, F. 1997; 23 Ashburner, J.; Friston, K. 2005; 26 Xiao, G.; Brady, M.; Noble, J.; Zhang, Y. 2002; 21 Brinkmann, B.; Manduca, A.; Robb, R. 1998; 17 Lewis, E.; Fox, N. 2004; 23 Sled, J.; Zijdenbos, A.; Evans, A. 1998; 17 Chen, C. 2005; 31 Pye, S.; Wild, S.; McDicken, W. 1992; 18 Chen, D.; Chang, R.; Wu, W.; Moon, M.; Wu, W. 2003; 29 Chang, R.; Wu, W.; Moon, W.; Chen, D. 2005; 89 Joo, S.; Yang, Y.; Moon, W.; Kim, H. 2004; 23 Horsch, K.; Giger, M.; Venta, L.; Vyborny, C. 2002; 29 Vovk, U.; Pernuš, F.; Likar, B. 2007; 26 Zhang, Y.; Brady, M.; Smith, S. 2001; 20 Ahmed, M.; Yamany, S.; Mohamed, N.; Farag, A.; Moriarty, T. 2002; 21 Vovk, U.; Pernuš, F.; Likar, B. 2004; 49 Jianbo, S.; Jitendra, M. 2000; 22 O'Donnell, M. 1983; SU-30 Dawant, B.; Zijdenbos, A.; Margolin, R. 1993; 12 1998; 17 1993; 12 2002; 29 2004; 49 2007; 242 2000; 22 2002; 21 2004; 23 1997; 23 1992; 18 2005; 31 2005 1999; 3 2003; 29 1983; SU-30 2005; 26 2005; 89 2007; 26 2001; 20 2001; 10 e_1_2_5_15_1 e_1_2_5_14_1 e_1_2_5_17_1 e_1_2_5_9_1 e_1_2_5_16_1 e_1_2_5_8_1 e_1_2_5_11_1 e_1_2_5_7_1 e_1_2_5_10_1 e_1_2_5_24_1 e_1_2_5_6_1 e_1_2_5_13_1 e_1_2_5_21_1 e_1_2_5_5_1 e_1_2_5_12_1 e_1_2_5_22_1 e_1_2_5_4_1 e_1_2_5_3_1 e_1_2_5_2_1 e_1_2_5_19_1 e_1_2_5_18_1 Shreedhara K. S. (e_1_2_5_23_1) 2005 e_1_2_5_20_1 |
| References_xml | – volume: 26 start-page: 405 issn: 0278-0062 year: 2007 ident: c5 article-title: A review of methods for correction of intensity inhomogeneity in MRI publication-title: IEEE Trans. Med. Imaging – volume: 10 start-page: 266 issn: 1057-7149 year: 2001 ident: c23 article-title: Active contours without edges publication-title: IEEE Trans. Image Process. – volume: 23 start-page: 1292 issn: 0278-0062 year: 2004 ident: c17 article-title: Computer-aided diagnosis of solid breast nodules: Use of an artificial neural network based on multiple sonographic features publication-title: IEEE Trans. Med. Imaging – volume: 89 start-page: 179 issn: 0167-6806 year: 2005 ident: c21 article-title: Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors publication-title: Breast Cancer Res. Treat. – volume: 22 start-page: 888 issn: 0162-8828 year: 2000 ident: c16 article-title: Normalized cuts and image segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 29 start-page: 157 issn: 0094-2405 year: 2002 ident: c18 article-title: Computerized diagnosis of breast lesions on ultrasound publication-title: Med. Phys. – volume: SU-30 start-page: 26 issn: 0018-9537 year: 1983 ident: c1 article-title: Quantitative volume backscatter imaging publication-title: IEEE Trans. Sonics Ultrason. – volume: 17 start-page: 161 issn: 0278-0062 year: 1998 ident: c6 article-title: Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction publication-title: IEEE Trans. Med. Imaging – volume: 31 start-page: 1647 issn: 0301-5629 year: 2005 ident: c15 article-title: Cell-competition algorithm: A new segmentation algorithm for multiple objects with irregular boundaries in ultrasound images publication-title: Ultrasound Med. Biol. – volume: 26 start-page: 839 issn: 1053-8119 year: 2005 ident: c11 article-title: Unified segmentation publication-title: Neuroimage – volume: 17 start-page: 87 issn: 0278-0062 year: 1998 ident: c8 article-title: A nonparametric method for automatic correction of intensity nonuniformity in MRI data publication-title: IEEE Trans. Med. Imaging – volume: 23 start-page: 651 issn: 0301-5629 year: 1997 ident: c3 article-title: Automatic attenuation compensation for ultrasonic imaging publication-title: Ultrasound Med. Biol. – volume: 18 start-page: 205 issn: 0301-5629 year: 1992 ident: c2 article-title: Adaptive time gain compensation for ultrasound imaging publication-title: Ultrasound Med. Biol. – volume: 23 start-page: 75 issn: 1053-8119 year: 2004 ident: c7 article-title: Correction of differential intensity inhomogeneity in longitudinal MR images publication-title: Neuroimage – volume: 29 start-page: 1017 issn: 0301-5629 year: 2003 ident: c20 article-title: 3-D breast ultrasound segmentation using active contour model publication-title: Ultrasound Med. Biol. – volume: 12 start-page: 770 issn: 0278-0062 year: 1993 ident: c12 article-title: Correction of intensity variations in MR images for computer-aided tissues classification publication-title: IEEE Trans. Med. Imaging – volume: 3 start-page: 409 issn: 1361-8415 year: 1999 ident: c13 article-title: A new variational shape-from-orientation approach to correcting intensity inhomogeneities in magnetic resonance images publication-title: Med. Image Anal. – volume: 20 start-page: 45 issn: 0278-0062 year: 2001 ident: c14 article-title: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm publication-title: IEEE Trans. Med. Imaging – volume: 49 start-page: 4119 issn: 0031-9155 year: 2004 ident: c9 article-title: MRI intensity inhomogeneity correction by combining intensity and spatial information publication-title: Phys. Med. Biol. – volume: 21 start-page: 193 issn: 0278-0062 year: 2002 ident: c10 article-title: A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data publication-title: IEEE Trans. Med. Imaging – volume: 21 start-page: 48 issn: 0278-0062 year: 2002 ident: c4 article-title: Segmentation of ultrasound B-mode images with intensity inhomogeneity correction publication-title: IEEE Trans. Med. Imaging – volume: 242 start-page: 716 issn: 0033-8419 year: 2007 ident: c19 article-title: Malignant and benign breast masses on 3D US volumetric images: Effect of computer-aided diagnosis on radiologist accuracy publication-title: Radiology – volume: 29 start-page: 157-164 year: 2002 publication-title: Med. Phys. doi: 10.1118/1.1429239 – volume: 26 start-page: 405-421 year: 2007 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2006.891486 – volume: 12 start-page: 770-781 year: 1993 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.251128 – volume: 21 start-page: 48-57 year: 2002 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.981233 – volume: 17 start-page: 161-171 year: 1998 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.700729 – volume: 18 start-page: 205-212 year: 1992 publication-title: Ultrasound Med. Biol. doi: 10.1016/0301-5629(92)90131-S – volume: 23 start-page: 75-83 year: 2004 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2004.04.030 – volume: 22 start-page: 888-905 year: 2000 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.868688 – volume: 31 start-page: 1647-1664 year: 2005 publication-title: Ultrasound Med. Biol. doi: 10.1016/j.ultrasmedbio.2005.09.011 – volume: 20 start-page: 45-57 year: 2001 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.906424 – volume: 242 start-page: 716-724 year: 2007 publication-title: Radiology doi: 10.1148/radiol.2423051464 – volume: 23 start-page: 1292-1300 year: 2004 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2004.834617 – volume: 10 start-page: 266-277 year: 2001 publication-title: IEEE Trans. Image Process. doi: 10.1109/83.902291 – volume: 49 start-page: 4119-4133 year: 2004 publication-title: Phys. Med. Biol. doi: 10.1088/0031-9155/49/17/020 – volume: 29 start-page: 1017-1026 year: 2003 publication-title: Ultrasound Med. Biol. doi: 10.1016/S0301-5629(03)00059-0 – volume: 23 start-page: 651-664 year: 1997 publication-title: Ultrasound Med. Biol. doi: 10.1016/S0301-5629(97)00002-1 – volume: 17 start-page: 87-97 year: 1998 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.668698 – volume: 26 start-page: 839-851 year: 2005 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2005.02.018 – volume: 3 start-page: 409-424 year: 1999 publication-title: Med. Image Anal. doi: 10.1016/S1361-8415(99)80033-4 – volume: SU-30 start-page: 26-36 year: 1983 publication-title: IEEE Trans. Sonics Ultrason. – volume: 21 start-page: 193-199 year: 2002 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.996338 – volume: 89 start-page: 179-185 year: 2005 publication-title: Breast Cancer Res. Treat. doi: 10.1007/s10549-004-2043-z – volume: 10 start-page: 266 issue: 2 year: 2001 end-page: 277 article-title: Active contours without edges publication-title: IEEE Trans. Image Process. – volume: 17 start-page: 87 issue: 1 year: 1998 end-page: 97 article-title: A nonparametric method for automatic correction of intensity nonuniformity in MRI data publication-title: IEEE Trans. Med. Imaging – volume: 23 start-page: 1292 issue: 10 year: 2004 end-page: 1300 article-title: Computer‐aided diagnosis of solid breast nodules: Use of an artificial neural network based on multiple sonographic features publication-title: IEEE Trans. Med. Imaging – volume: 20 start-page: 45 year: 2001 end-page: 57 article-title: Segmentation of brain MR images through a hidden Markov random field model and the expectation‐maximization algorithm publication-title: IEEE Trans. Med. Imaging – volume: 29 start-page: 1017 issue: 7 year: 2003 end-page: 1026 article-title: 3‐D breast ultrasound segmentation using active contour model publication-title: Ultrasound Med. Biol. – volume: 21 start-page: 193 issue: 3 year: 2002 end-page: 199 article-title: A modified fuzzy C‐means algorithm for bias field estimation and segmentation of MRI data publication-title: IEEE Trans. Med. Imaging – volume: 29 start-page: 157 issue: 2 year: 2002 end-page: 164 article-title: Computerized diagnosis of breast lesions on ultrasound publication-title: Med. Phys. – volume: 3 start-page: 409 year: 1999 end-page: 424 article-title: A new variational shape‐from‐orientation approach to correcting intensity inhomogeneities in magnetic resonance images publication-title: Med. Image Anal. – volume: 18 start-page: 205 issue: 2 year: 1992 end-page: 212 article-title: Adaptive time gain compensation for ultrasound imaging publication-title: Ultrasound Med. Biol. – volume: 26 start-page: 405 issue: 3 year: 2007 end-page: 421 article-title: A review of methods for correction of intensity inhomogeneity in MRI publication-title: IEEE Trans. Med. Imaging – volume: 12 start-page: 770 issue: 4 year: 1993 end-page: 781 article-title: Correction of intensity variations in MR images for computer‐aided tissues classification publication-title: IEEE Trans. Med. Imaging – volume: SU-30 start-page: 26 year: 1983 end-page: 36 article-title: Quantitative volume backscatter imaging publication-title: IEEE Trans. Sonics Ultrason. – volume: 26 start-page: 839 year: 2005 end-page: 851 article-title: Unified segmentation publication-title: Neuroimage – start-page: 759 year: 2005 end-page: 767 article-title: 3D reconstruction of solid breast nodule in ultrasonographic image – volume: 17 start-page: 161 issue: 2 year: 1998 end-page: 171 article-title: Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction publication-title: IEEE Trans. Med. Imaging – volume: 23 start-page: 75 year: 2004 end-page: 83 article-title: Correction of differential intensity inhomogeneity in longitudinal MR images publication-title: Neuroimage – volume: 242 start-page: 716 year: 2007 end-page: 724 article-title: Malignant and benign breast masses on 3D US volumetric images: Effect of computer‐aided diagnosis on radiologist accuracy publication-title: Radiology – volume: 23 start-page: 651 issue: 5 year: 1997 end-page: 664 article-title: Automatic attenuation compensation for ultrasonic imaging publication-title: Ultrasound Med. Biol. – volume: 89 start-page: 179 year: 2005 end-page: 185 article-title: Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors publication-title: Breast Cancer Res. Treat. – volume: 21 start-page: 48 issue: 1 year: 2002 end-page: 57 article-title: Segmentation of ultrasound B‐mode images with intensity inhomogeneity correction publication-title: IEEE Trans. Med. Imaging – volume: 49 start-page: 4119 year: 2004 end-page: 4133 article-title: MRI intensity inhomogeneity correction by combining intensity and spatial information publication-title: Phys. Med. Biol. – volume: 31 start-page: 1647 issue: 12 year: 2005 end-page: 1664 article-title: Cell‐competition algorithm: A new segmentation algorithm for multiple objects with irregular boundaries in ultrasound images publication-title: Ultrasound Med. Biol. – volume: 22 start-page: 888 issue: 8 year: 2000 end-page: 905 article-title: Normalized cuts and image segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – ident: e_1_2_5_5_1 doi: 10.1109/42.981233 – start-page: 759 year: 2005 ident: e_1_2_5_23_1 – ident: e_1_2_5_22_1 doi: 10.1007/s10549-004-2043-z – ident: e_1_2_5_20_1 doi: 10.1148/radiol.2423051464 – ident: e_1_2_5_7_1 doi: 10.1109/42.700729 – ident: e_1_2_5_17_1 doi: 10.1109/34.868688 – ident: e_1_2_5_18_1 doi: 10.1109/TMI.2004.834617 – ident: e_1_2_5_19_1 doi: 10.1118/1.1429239 – ident: e_1_2_5_9_1 doi: 10.1109/42.668698 – ident: e_1_2_5_10_1 doi: 10.1088/0031-9155/49/17/020 – ident: e_1_2_5_15_1 doi: 10.1109/42.906424 – ident: e_1_2_5_21_1 doi: 10.1016/S0301-5629(03)00059-0 – ident: e_1_2_5_16_1 doi: 10.1016/j.ultrasmedbio.2005.09.011 – ident: e_1_2_5_13_1 doi: 10.1109/42.251128 – ident: e_1_2_5_4_1 doi: 10.1016/S0301-5629(97)00002-1 – ident: e_1_2_5_2_1 doi: 10.1109/T-SU.1983.31379 – ident: e_1_2_5_24_1 doi: 10.1109/83.902291 – ident: e_1_2_5_6_1 doi: 10.1109/TMI.2006.891486 – ident: e_1_2_5_11_1 doi: 10.1109/42.996338 – ident: e_1_2_5_12_1 doi: 10.1016/j.neuroimage.2005.02.018 – ident: e_1_2_5_14_1 doi: 10.1016/S1361-8415(99)80033-4 – ident: e_1_2_5_3_1 doi: 10.1016/0301-5629(92)90131-S – ident: e_1_2_5_8_1 doi: 10.1016/j.neuroimage.2004.04.030 |
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To develop an intensity inhomogeneity algorithm for breast sonograms in order to assist visual identification and automatic delineation of lesion... To develop an intensity inhomogeneity algorithm for breast sonograms in order to assist visual identification and automatic delineation of lesion boundaries.... To develop an intensity inhomogeneity algorithm for breast sonograms in order to assist visual identification and automatic delineation of lesion... |
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| SubjectTerms | 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 |
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