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Vascular tree segmentation in medical images using Hessian-based multiscale filtering and level set method.

Jin J, Yang L, Zhang X, Ding M - Comput Math Methods Med (2013)

Bottom Line: Then Hessian-based multiscale filtering is used to enhance vascular structures by combining Hessian matrix with Gaussian convolution to tune the filtering response to the specific scales.Our approach was tested on synthetic images with vascular-like structures and 2D slices extracted from real 3D abdomen magnetic resonance angiography (MRA) images along the coronal plane.The segmentation rates for synthetic images are above 95%.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, School of Life Science and Technology, Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China.

ABSTRACT
Vascular segmentation plays an important role in medical image analysis. A novel technique for the automatic extraction of vascular trees from 2D medical images is presented, which combines Hessian-based multiscale filtering and a modified level set method. In the proposed algorithm, the morphological top-hat transformation is firstly adopted to attenuate background. Then Hessian-based multiscale filtering is used to enhance vascular structures by combining Hessian matrix with Gaussian convolution to tune the filtering response to the specific scales. Because Gaussian convolution tends to blur vessel boundaries, which makes scale selection inaccurate, an improved level set method is finally proposed to extract vascular structures by introducing an external constrained term related to the standard deviation of Gaussian function into the traditional level set. Our approach was tested on synthetic images with vascular-like structures and 2D slices extracted from real 3D abdomen magnetic resonance angiography (MRA) images along the coronal plane. The segmentation rates for synthetic images are above 95%. The results for MRA images demonstrate that the proposed method can extract most of the vascular structures successfully and accurately in visualization. Therefore, the proposed method is effective for the vascular tree extraction in medical images.

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Related in: MedlinePlus

Analysis of noise sensitivity. (a) Original image. (b) Influence of noise levels.
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fig4: Analysis of noise sensitivity. (a) Original image. (b) Influence of noise levels.

Mentions: To investigate the sensitivity of the proposed method to noise, we used the synthetic image of size 256 × 256 with a vessel-like structure of varying width and orientation in Figure 4(a), and added zero mean Gaussian noise of standard deviations ranging from 5 to 30 to this image. Figure 4(b) shows the segmentation rate for Gaussian noise of the various standard deviations. It is easy to see from Figure 4(b) that the segmentation rate decreases slightly but remains above 97% with increasing noise levels in the image. Figure 5 shows the segmentation results under different noise levels. Obviously, the proposed method is robust to noise in that it can extract the tree structure effectively at the various noise levels.


Vascular tree segmentation in medical images using Hessian-based multiscale filtering and level set method.

Jin J, Yang L, Zhang X, Ding M - Comput Math Methods Med (2013)

Analysis of noise sensitivity. (a) Original image. (b) Influence of noise levels.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3852584&req=5

fig4: Analysis of noise sensitivity. (a) Original image. (b) Influence of noise levels.
Mentions: To investigate the sensitivity of the proposed method to noise, we used the synthetic image of size 256 × 256 with a vessel-like structure of varying width and orientation in Figure 4(a), and added zero mean Gaussian noise of standard deviations ranging from 5 to 30 to this image. Figure 4(b) shows the segmentation rate for Gaussian noise of the various standard deviations. It is easy to see from Figure 4(b) that the segmentation rate decreases slightly but remains above 97% with increasing noise levels in the image. Figure 5 shows the segmentation results under different noise levels. Obviously, the proposed method is robust to noise in that it can extract the tree structure effectively at the various noise levels.

Bottom Line: Then Hessian-based multiscale filtering is used to enhance vascular structures by combining Hessian matrix with Gaussian convolution to tune the filtering response to the specific scales.Our approach was tested on synthetic images with vascular-like structures and 2D slices extracted from real 3D abdomen magnetic resonance angiography (MRA) images along the coronal plane.The segmentation rates for synthetic images are above 95%.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, School of Life Science and Technology, Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China.

ABSTRACT
Vascular segmentation plays an important role in medical image analysis. A novel technique for the automatic extraction of vascular trees from 2D medical images is presented, which combines Hessian-based multiscale filtering and a modified level set method. In the proposed algorithm, the morphological top-hat transformation is firstly adopted to attenuate background. Then Hessian-based multiscale filtering is used to enhance vascular structures by combining Hessian matrix with Gaussian convolution to tune the filtering response to the specific scales. Because Gaussian convolution tends to blur vessel boundaries, which makes scale selection inaccurate, an improved level set method is finally proposed to extract vascular structures by introducing an external constrained term related to the standard deviation of Gaussian function into the traditional level set. Our approach was tested on synthetic images with vascular-like structures and 2D slices extracted from real 3D abdomen magnetic resonance angiography (MRA) images along the coronal plane. The segmentation rates for synthetic images are above 95%. The results for MRA images demonstrate that the proposed method can extract most of the vascular structures successfully and accurately in visualization. Therefore, the proposed method is effective for the vascular tree extraction in medical images.

Show MeSH
Related in: MedlinePlus