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A probabilistic approach for breast boundary extraction in mammograms.

Habibi Aghdam H, Puig D, Solanas A - Comput Math Methods Med (2013)

Bottom Line: On the other hand, active contour models require defining a starting point close to the actual boundary to be able to successfully extract the boundary.In addition, the smoothness of the boundary is handled by using a new probability model.Experimental results show that the proposed method reaches 38% and 50% improvement with respect to the results obtained by the active contour model and threshold-based methods respectively, and it increases the stability of the boundary extraction process up to 86%.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering and Mathematics, Rovira i Virgili University, 43007 Tarragona, Spain.

ABSTRACT
The extraction of the breast boundary is crucial to perform further analysis of mammogram. Methods to extract the breast boundary can be classified into two categories: methods based on image processing techniques and those based on models. The former use image transformation techniques such as thresholding, morphological operations, and region growing. In the second category, the boundary is extracted using more advanced techniques, such as the active contour model. The problem with thresholding methods is that it is a hard to automatically find the optimal threshold value by using histogram information. On the other hand, active contour models require defining a starting point close to the actual boundary to be able to successfully extract the boundary. In this paper, we propose a probabilistic approach to address the aforementioned problems. In our approach we use local binary patterns to describe the texture around each pixel. In addition, the smoothness of the boundary is handled by using a new probability model. Experimental results show that the proposed method reaches 38% and 50% improvement with respect to the results obtained by the active contour model and threshold-based methods respectively, and it increases the stability of the boundary extraction process up to 86%.

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Related in: MedlinePlus

Result of applying the algorithm on several images from mini-MIAS database.
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Related In: Results  -  Collection


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fig20: Result of applying the algorithm on several images from mini-MIAS database.

Mentions: Finally, Figure 20 shows some global results produced by our proposed method. This figure depicts the extracted breast and pectoral muscle boundaries for a variety of mammograms (fatty, fatty glandular, and dense glandular mammograms). From these illustrations, it is feasible to appreciate the accuracy of the algorithm presented in this paper to extract the breast region from the background and the pectoral muscle.


A probabilistic approach for breast boundary extraction in mammograms.

Habibi Aghdam H, Puig D, Solanas A - Comput Math Methods Med (2013)

Result of applying the algorithm on several images from mini-MIAS database.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3842063&req=5

fig20: Result of applying the algorithm on several images from mini-MIAS database.
Mentions: Finally, Figure 20 shows some global results produced by our proposed method. This figure depicts the extracted breast and pectoral muscle boundaries for a variety of mammograms (fatty, fatty glandular, and dense glandular mammograms). From these illustrations, it is feasible to appreciate the accuracy of the algorithm presented in this paper to extract the breast region from the background and the pectoral muscle.

Bottom Line: On the other hand, active contour models require defining a starting point close to the actual boundary to be able to successfully extract the boundary.In addition, the smoothness of the boundary is handled by using a new probability model.Experimental results show that the proposed method reaches 38% and 50% improvement with respect to the results obtained by the active contour model and threshold-based methods respectively, and it increases the stability of the boundary extraction process up to 86%.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering and Mathematics, Rovira i Virgili University, 43007 Tarragona, Spain.

ABSTRACT
The extraction of the breast boundary is crucial to perform further analysis of mammogram. Methods to extract the breast boundary can be classified into two categories: methods based on image processing techniques and those based on models. The former use image transformation techniques such as thresholding, morphological operations, and region growing. In the second category, the boundary is extracted using more advanced techniques, such as the active contour model. The problem with thresholding methods is that it is a hard to automatically find the optimal threshold value by using histogram information. On the other hand, active contour models require defining a starting point close to the actual boundary to be able to successfully extract the boundary. In this paper, we propose a probabilistic approach to address the aforementioned problems. In our approach we use local binary patterns to describe the texture around each pixel. In addition, the smoothness of the boundary is handled by using a new probability model. Experimental results show that the proposed method reaches 38% and 50% improvement with respect to the results obtained by the active contour model and threshold-based methods respectively, and it increases the stability of the boundary extraction process up to 86%.

Show MeSH
Related in: MedlinePlus