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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

Shape-based growth strategy
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Fig4: Shape-based growth strategy

Mentions: The pectoral muscle boundary is often obtained by refining a straight line using intensity information [7, 15]. However, the estimated straight line seriously affects the extraction accuracy of the pectoral muscle boundary. In this study, to get an accurate boundary of the pectoral muscle, a simple and convenient boundary detection method based on the start point is proposed to segment the pectoral muscle in a mammogram. Based on the characteristics of the pectoral muscle, a shape-based growth mask is designed as shown in Fig. 4, in which S and C represent the current seed point and candidate point, respectively. The number of candidates is and the row interval is. The proper selection of can reduce the effects of noise and fibro-glandular tissues. Different from traditional region growth methods, most of the candidates are placed on the left side of the current seed point to match the shape of the pectoral muscle, which gradually narrows from top to bottom. The start point is defined as the first seed point. The candidate point with the maximum value is then selected as the next seed point. The process is iterated until the new seed point is close enough to the left side of the image. All seed points produced by the shape-based growth mask are fitted by a cubic polynomial function to create a boundary.Fig. 4


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

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

Shape-based growth strategy
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: Shape-based growth strategy
Mentions: The pectoral muscle boundary is often obtained by refining a straight line using intensity information [7, 15]. However, the estimated straight line seriously affects the extraction accuracy of the pectoral muscle boundary. In this study, to get an accurate boundary of the pectoral muscle, a simple and convenient boundary detection method based on the start point is proposed to segment the pectoral muscle in a mammogram. Based on the characteristics of the pectoral muscle, a shape-based growth mask is designed as shown in Fig. 4, in which S and C represent the current seed point and candidate point, respectively. The number of candidates is and the row interval is. The proper selection of can reduce the effects of noise and fibro-glandular tissues. Different from traditional region growth methods, most of the candidates are placed on the left side of the current seed point to match the shape of the pectoral muscle, which gradually narrows from top to bottom. The start point is defined as the first seed point. The candidate point with the maximum value is then selected as the next seed point. The process is iterated until the new seed point is close enough to the left side of the image. All seed points produced by the shape-based growth mask are fitted by a cubic polynomial function to create a boundary.Fig. 4

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