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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

Illustration of pectoral muscle detection
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Fig1: Illustration of pectoral muscle detection

Mentions: The pectoral muscle has significant anatomical features, such as sharp intensity changes on the boundary, roughly triangular shape, and gradually narrowing from top to bottom [17]. Based on these characteristics, an SBEM and a boundary evolution strategy are proposed in this paper to automatically detect the pectoral muscle boundary, as shown in Fig. 1.Fig. 1


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

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

Illustration of pectoral muscle detection
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: Illustration of pectoral muscle detection
Mentions: The pectoral muscle has significant anatomical features, such as sharp intensity changes on the boundary, roughly triangular shape, and gradually narrowing from top to bottom [17]. Based on these characteristics, an SBEM and a boundary evolution strategy are proposed in this paper to automatically detect the pectoral muscle boundary, as shown in Fig. 1.Fig. 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