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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

Pectoral muscle detection results of MLO mammograms a mdb002, b mdb123, c mdb110, d mdb050, e mdb225, f mdb053, g mdb288, h mdb151, i mdb240, j mdb223, and k mdb183
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Fig6: Pectoral muscle detection results of MLO mammograms a mdb002, b mdb123, c mdb110, d mdb050, e mdb225, f mdb053, g mdb288, h mdb151, i mdb240, j mdb223, and k mdb183

Mentions: Some representative pectoral muscle detection results of MLO mammograms are shown in Fig. 6. To evaluate the quality of the pectoral muscle detection method, the boundaries of each image were manually drawn by the author, and checked by two radiologists individually. When differences existed, a consensus was reached after discussion. These manual contours were then used as the ground truth. The boundary extraction results were classified into three categories: successful, acceptable, and unacceptable. For successful results, the detected boundary was identical to the manual one. For acceptable results, more than half of the muscle boundary was correct, with and only limited discrepancy for the lower half part. All other results were unacceptable. Table 2 lists the detection results for the 322 mammograms from the mini-MIAS database. 97.2 % of the results were successful or acceptable. Furthermore, 84 mammograms used by Ferrari [9] were selected for quantitative evaluation. The mean FP and FN rates were 1.02 and 5.63 %, respectively, and the mean and standard deviation of the Hausdorff distance were 3.53 and 1.61, respectively. The FP, FN, and Hausorff distance values for various methods are compared in Table 3.Fig. 6


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

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

Pectoral muscle detection results of MLO mammograms a mdb002, b mdb123, c mdb110, d mdb050, e mdb225, f mdb053, g mdb288, h mdb151, i mdb240, j mdb223, and k mdb183
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4491117&req=5

Fig6: Pectoral muscle detection results of MLO mammograms a mdb002, b mdb123, c mdb110, d mdb050, e mdb225, f mdb053, g mdb288, h mdb151, i mdb240, j mdb223, and k mdb183
Mentions: Some representative pectoral muscle detection results of MLO mammograms are shown in Fig. 6. To evaluate the quality of the pectoral muscle detection method, the boundaries of each image were manually drawn by the author, and checked by two radiologists individually. When differences existed, a consensus was reached after discussion. These manual contours were then used as the ground truth. The boundary extraction results were classified into three categories: successful, acceptable, and unacceptable. For successful results, the detected boundary was identical to the manual one. For acceptable results, more than half of the muscle boundary was correct, with and only limited discrepancy for the lower half part. All other results were unacceptable. Table 2 lists the detection results for the 322 mammograms from the mini-MIAS database. 97.2 % of the results were successful or acceptable. Furthermore, 84 mammograms used by Ferrari [9] were selected for quantitative evaluation. The mean FP and FN rates were 1.02 and 5.63 %, respectively, and the mean and standard deviation of the Hausdorff distance were 3.53 and 1.61, respectively. The FP, FN, and Hausorff distance values for various methods are compared in Table 3.Fig. 6

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