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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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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