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An active contour model for the segmentation of images with intensity inhomogeneities and bias field estimation.

Huang C, Zeng L - PLoS ONE (2015)

Bottom Line: The proposed model first appeared as a two-phase model and then extended to a multi-phase one.The experimental results demonstrate the advantages of our model in terms of accuracy and insensitivity to the location of the initial contours.In particular, our method has been applied to various synthetic and real images with desirable results.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing University, Chongqing, 400044, China; Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing, 400044, China.

ABSTRACT
Intensity inhomogeneity causes many difficulties in image segmentation and the understanding of magnetic resonance (MR) images. Bias correction is an important method for addressing the intensity inhomogeneity of MR images before quantitative analysis. In this paper, a modified model is developed for segmenting images with intensity inhomogeneity and estimating the bias field simultaneously. In the modified model, a clustering criterion energy function is defined by considering the difference between the measured image and estimated image in local region. By using this difference in local region, the modified method can obtain accurate segmentation results and an accurate estimation of the bias field. The energy function is incorporated into a level set formulation with a level set regularization term, and the energy minimization is conducted by a level set evolution process. The proposed model first appeared as a two-phase model and then extended to a multi-phase one. The experimental results demonstrate the advantages of our model in terms of accuracy and insensitivity to the location of the initial contours. In particular, our method has been applied to various synthetic and real images with desirable results.

No MeSH data available.


Experimental results of our model.(a), (e) The original image with red initial contours. (b), (f) The final segmentation results of our model. (c), (g) The estimation of the bias field. (d), (h) The corrected image. (i) The histogram of the original image. (j) The histogram of the corrected image with initial contour (a). (k) The histogram of the corrected image with initial contour (e).
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pone.0120399.g005: Experimental results of our model.(a), (e) The original image with red initial contours. (b), (f) The final segmentation results of our model. (c), (g) The estimation of the bias field. (d), (h) The corrected image. (i) The histogram of the original image. (j) The histogram of the corrected image with initial contour (a). (k) The histogram of the corrected image with initial contour (e).

Mentions: Fig. 5 shows the segmentation results and bias field correction of the synthetic image with intensity inhomogeneity shown in Fig. 3 obtained by our model. For the distinct initial contours in Figs. 5(a) and 5(e), the corrected images are shown in Figs. 5(d) and 5(h), and the histograms of bias corrected image for different initial contours are shown in Fig. 5(j) and 5(k), which results in higher-quality image than the original image (Fig. 5(g)). The two histograms of the bias-corrected image with different initial contours are nearly identical.


An active contour model for the segmentation of images with intensity inhomogeneities and bias field estimation.

Huang C, Zeng L - PLoS ONE (2015)

Experimental results of our model.(a), (e) The original image with red initial contours. (b), (f) The final segmentation results of our model. (c), (g) The estimation of the bias field. (d), (h) The corrected image. (i) The histogram of the original image. (j) The histogram of the corrected image with initial contour (a). (k) The histogram of the corrected image with initial contour (e).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4383562&req=5

pone.0120399.g005: Experimental results of our model.(a), (e) The original image with red initial contours. (b), (f) The final segmentation results of our model. (c), (g) The estimation of the bias field. (d), (h) The corrected image. (i) The histogram of the original image. (j) The histogram of the corrected image with initial contour (a). (k) The histogram of the corrected image with initial contour (e).
Mentions: Fig. 5 shows the segmentation results and bias field correction of the synthetic image with intensity inhomogeneity shown in Fig. 3 obtained by our model. For the distinct initial contours in Figs. 5(a) and 5(e), the corrected images are shown in Figs. 5(d) and 5(h), and the histograms of bias corrected image for different initial contours are shown in Fig. 5(j) and 5(k), which results in higher-quality image than the original image (Fig. 5(g)). The two histograms of the bias-corrected image with different initial contours are nearly identical.

Bottom Line: The proposed model first appeared as a two-phase model and then extended to a multi-phase one.The experimental results demonstrate the advantages of our model in terms of accuracy and insensitivity to the location of the initial contours.In particular, our method has been applied to various synthetic and real images with desirable results.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing University, Chongqing, 400044, China; Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing, 400044, China.

ABSTRACT
Intensity inhomogeneity causes many difficulties in image segmentation and the understanding of magnetic resonance (MR) images. Bias correction is an important method for addressing the intensity inhomogeneity of MR images before quantitative analysis. In this paper, a modified model is developed for segmenting images with intensity inhomogeneity and estimating the bias field simultaneously. In the modified model, a clustering criterion energy function is defined by considering the difference between the measured image and estimated image in local region. By using this difference in local region, the modified method can obtain accurate segmentation results and an accurate estimation of the bias field. The energy function is incorporated into a level set formulation with a level set regularization term, and the energy minimization is conducted by a level set evolution process. The proposed model first appeared as a two-phase model and then extended to a multi-phase one. The experimental results demonstrate the advantages of our model in terms of accuracy and insensitivity to the location of the initial contours. In particular, our method has been applied to various synthetic and real images with desirable results.

No MeSH data available.