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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

The vascular tree model with different complexities. (a) A branch. (b) Two branches. (c) Four branches. (d) Six branches. (e) Eight branches.
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fig2: The vascular tree model with different complexities. (a) A branch. (b) Two branches. (c) Four branches. (d) Six branches. (e) Eight branches.

Mentions: Figure 1 shows three types of synthetic images of size 512 × 512. In Figure 1(a), the diameters of the simulated vascular structures range from 1 pixel to 15 pixels. In Figure 1(b), directions of the vascular structures are simulated by counter-clockwise rotation with an interval of 30 degrees starting from the vertical direction. In Figure 1(c), the intensities of the vessels from left to right are set between 32 to 256 with an increment of 32, while the intensity of background is 0. Meanwhile, five vascular tree models with different complexities are presented in Figure 2.


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)

The vascular tree model with different complexities. (a) A branch. (b) Two branches. (c) Four branches. (d) Six branches. (e) Eight branches.
© Copyright Policy
Related In: Results  -  Collection

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

fig2: The vascular tree model with different complexities. (a) A branch. (b) Two branches. (c) Four branches. (d) Six branches. (e) Eight branches.
Mentions: Figure 1 shows three types of synthetic images of size 512 × 512. In Figure 1(a), the diameters of the simulated vascular structures range from 1 pixel to 15 pixels. In Figure 1(b), directions of the vascular structures are simulated by counter-clockwise rotation with an interval of 30 degrees starting from the vertical direction. In Figure 1(c), the intensities of the vessels from left to right are set between 32 to 256 with an increment of 32, while the intensity of background is 0. Meanwhile, five vascular tree models with different complexities are presented in Figure 2.

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