Limits...
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%.

Show MeSH

Related in: MedlinePlus

Importance of initialization in deformable models.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3842063&req=5

fig2: Importance of initialization in deformable models.

Mentions: Technically, active contour models and level-set methods are applicable techniques for medical image segmentation, but they suffer from poor initialization. The main issue of those methods is that their accuracy depends on their initialization. In the case of mammograms, this kind of methods is usually initialized using thresholding techniques. As a result, they are vulnerable to remaining stuck in local optima rather than in the actual boundary. Figure 2 illustrates this problem: the two graphs at the bottom of the figure show a small portion of the external force surface containing lots of local minima. It is apparent from these images that, if the algorithm is initialized a few pixels away from the correct minimum, the resulting boundary will be settled in a local minimum that does not correspond with the actual boundary.


A probabilistic approach for breast boundary extraction in mammograms.

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

Importance of initialization in deformable models.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Importance of initialization in deformable models.
Mentions: Technically, active contour models and level-set methods are applicable techniques for medical image segmentation, but they suffer from poor initialization. The main issue of those methods is that their accuracy depends on their initialization. In the case of mammograms, this kind of methods is usually initialized using thresholding techniques. As a result, they are vulnerable to remaining stuck in local optima rather than in the actual boundary. Figure 2 illustrates this problem: the two graphs at the bottom of the figure show a small portion of the external force surface containing lots of local minima. It is apparent from these images that, if the algorithm is initialized a few pixels away from the correct minimum, the resulting boundary will be settled in a local minimum that does not correspond with the actual boundary.

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