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Shape-based Automatic Detection of Pectoral Muscle Boundary in Mammograms.

Chen C, Liu G, Wang J, Sudlow G - J Med Biol Eng (2015)

Bottom Line: The seed point is then detected on the initial boundary and the pectoral boundary is evolved from candidate points produced using a shape-based growth strategy.A cubic polynomial fitting function is implemented to obtain the final pectoral muscle boundary.A 97.2 % acceptable rate from expert radiologists and assessment results based on the false positive rate, false negative rate, and Hausdorff distance demonstrate the robustness and effectiveness of the proposed shape-based detection method.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016 China.

ABSTRACT

The detection of the pectoral muscle boundary in the medio-lateral oblique view of mammograms is essential to improving the computer-aided diagnosis of breast cancer. In this study, a shape-based detection method is proposed for accurately extracting the boundary of the pectoral muscle in mammograms. A shape-based enhancement mask is applied to the mammogram and the initial boundary is then defined using morphological operators. The seed point is then detected on the initial boundary and the pectoral boundary is evolved from candidate points produced using a shape-based growth strategy. A cubic polynomial fitting function is implemented to obtain the final pectoral muscle boundary. The proposed method was applied to 322 mammograms from the mini Mammographic Image Analysis Society database. A 97.2 % acceptable rate from expert radiologists and assessment results based on the false positive rate, false negative rate, and Hausdorff distance demonstrate the robustness and effectiveness of the proposed shape-based detection method.

No MeSH data available.


Related in: MedlinePlus

a Mammogram mdb065 and results of b–d gradient image and e–g edges.  is 1.0, 0.5, and 0.0, respectively
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Fig5: a Mammogram mdb065 and results of b–d gradient image and e–g edges. is 1.0, 0.5, and 0.0, respectively

Mentions: 322 mammograms were processed using the SBEM. Table 1 lists the parameters used in this study. Parameters and were set to 1, and and were set to 2 to increase the suppression of tissues away from the boundary. In order to keep the sum of coefficients (excluding ) at zero, and were set to zero in the shifted enhancement mask. Parameter represents the current pixel’s intensity contribution. Normally, a larger value of leads to better highlighting of the pectoral muscle boundary. However, as a mammogram has multiple layers and the intensities of the inside layers are much stronger than that of the outside layer (Fig. 5a), a shape-based mask sometimes cannot enhance the real edge of the pectoral muscle. Figures 5(b–d) show enhanced images with various values of , and Fig. 5(e–g) show the detected edges with  = 100,  = 12, and various values. In order to effectively enhance the boundary of the outside layer, as a compromise, is generally set to 0.5. and depend on the size and spatial resolution of the image. and are determined from experiments. The proper selection of can reduce the interference introduced by noise and fibro-glandular tissue.Table 1


Shape-based Automatic Detection of Pectoral Muscle Boundary in Mammograms.

Chen C, Liu G, Wang J, Sudlow G - J Med Biol Eng (2015)

a Mammogram mdb065 and results of b–d gradient image and e–g edges.  is 1.0, 0.5, and 0.0, respectively
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4491117&req=5

Fig5: a Mammogram mdb065 and results of b–d gradient image and e–g edges. is 1.0, 0.5, and 0.0, respectively
Mentions: 322 mammograms were processed using the SBEM. Table 1 lists the parameters used in this study. Parameters and were set to 1, and and were set to 2 to increase the suppression of tissues away from the boundary. In order to keep the sum of coefficients (excluding ) at zero, and were set to zero in the shifted enhancement mask. Parameter represents the current pixel’s intensity contribution. Normally, a larger value of leads to better highlighting of the pectoral muscle boundary. However, as a mammogram has multiple layers and the intensities of the inside layers are much stronger than that of the outside layer (Fig. 5a), a shape-based mask sometimes cannot enhance the real edge of the pectoral muscle. Figures 5(b–d) show enhanced images with various values of , and Fig. 5(e–g) show the detected edges with  = 100,  = 12, and various values. In order to effectively enhance the boundary of the outside layer, as a compromise, is generally set to 0.5. and depend on the size and spatial resolution of the image. and are determined from experiments. The proper selection of can reduce the interference introduced by noise and fibro-glandular tissue.Table 1

Bottom Line: The seed point is then detected on the initial boundary and the pectoral boundary is evolved from candidate points produced using a shape-based growth strategy.A cubic polynomial fitting function is implemented to obtain the final pectoral muscle boundary.A 97.2 % acceptable rate from expert radiologists and assessment results based on the false positive rate, false negative rate, and Hausdorff distance demonstrate the robustness and effectiveness of the proposed shape-based detection method.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016 China.

ABSTRACT

The detection of the pectoral muscle boundary in the medio-lateral oblique view of mammograms is essential to improving the computer-aided diagnosis of breast cancer. In this study, a shape-based detection method is proposed for accurately extracting the boundary of the pectoral muscle in mammograms. A shape-based enhancement mask is applied to the mammogram and the initial boundary is then defined using morphological operators. The seed point is then detected on the initial boundary and the pectoral boundary is evolved from candidate points produced using a shape-based growth strategy. A cubic polynomial fitting function is implemented to obtain the final pectoral muscle boundary. The proposed method was applied to 322 mammograms from the mini Mammographic Image Analysis Society database. A 97.2 % acceptable rate from expert radiologists and assessment results based on the false positive rate, false negative rate, and Hausdorff distance demonstrate the robustness and effectiveness of the proposed shape-based detection method.

No MeSH data available.


Related in: MedlinePlus