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A Novel Active Contour Model for MRI Brain Segmentation used in Radiotherapy Treatment Planning.

Mostaar A, Houshyari M, Badieyan S - Electron Physician (2016)

Bottom Line: The energy function was reduced by about 5 and 7% after 70 and 430 iterations, respectively.These results showed that, with increasing iterations, the energy function decreased, but it decreased faster during the early iterations, after which it decreased slowly.An active contour model based on the energy function is a useful tool for medical image segmentation.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

ABSTRACT

Introduction: Brain image segmentation is one of the most important clinical tools used in radiology and radiotherapy. But accurate segmentation is a very difficult task because these images mostly contain noise, inhomogeneities, and sometimes aberrations. The purpose of this study was to introduce a novel, locally statistical active contour model (ACM) for magnetic resonance image segmentation in the presence of intense inhomogeneity with the ability to determine the position of contour and energy diagram.

Methods: A Gaussian distribution model with different means and variances was used for inhomogeneity, and a moving window was used to map the original image into another domain in which the intensity distributions of inhomogeneous objects were still Gaussian but were better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field by the original signal within the window. Then, a statistical energy function is defined for each local region. Also, to evaluate the performance of our method, experiments were conducted on MR images of the brain for segment tumors or normal tissue as visualization and energy functions.

Results: In the proposed method, we were able to determine the size and position of the initial contour and to count iterations to have a better segmentation. The energy function for 20 to 430 iterations was calculated. The energy function was reduced by about 5 and 7% after 70 and 430 iterations, respectively. These results showed that, with increasing iterations, the energy function decreased, but it decreased faster during the early iterations, after which it decreased slowly. Also, this method enables us to stop the segmentation based on the threshold that we define for the energy equation.

Conclusion: An active contour model based on the energy function is a useful tool for medical image segmentation. The proposed method combined the information about neighboring pixels that belonged to the same class, thereby making it strong to separate the desired objects from the background.

No MeSH data available.


Related in: MedlinePlus

(a) Selecting a proper initial contour; (b) good segmentation; (c) an improper initial contour; (d) results fault segmentation
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f2-epj-08-2443: (a) Selecting a proper initial contour; (b) good segmentation; (c) an improper initial contour; (d) results fault segmentation

Mentions: Figure 1 shows that this method can be applied for four MR images with intensity inhomogeneity. At first, the contour is applied on the center of images, and the image is shown after some iterations. In the other images, we changed the position and size of the contour, and the segmentation results are shown. The ability to choose the position and size of the initial contour helps us to decrease the number of iterations and the calculations; therefore the program can be performed very much faster, and it determines the position of tumor with higher accuracy. In Figure 2, we compared the result of segmentations for two different sizes and positions of contour for the same picture and same iterations. The proposed model performs well and handles high non-uniformity without degrading the final segmentation results.


A Novel Active Contour Model for MRI Brain Segmentation used in Radiotherapy Treatment Planning.

Mostaar A, Houshyari M, Badieyan S - Electron Physician (2016)

(a) Selecting a proper initial contour; (b) good segmentation; (c) an improper initial contour; (d) results fault segmentation
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4930267&req=5

f2-epj-08-2443: (a) Selecting a proper initial contour; (b) good segmentation; (c) an improper initial contour; (d) results fault segmentation
Mentions: Figure 1 shows that this method can be applied for four MR images with intensity inhomogeneity. At first, the contour is applied on the center of images, and the image is shown after some iterations. In the other images, we changed the position and size of the contour, and the segmentation results are shown. The ability to choose the position and size of the initial contour helps us to decrease the number of iterations and the calculations; therefore the program can be performed very much faster, and it determines the position of tumor with higher accuracy. In Figure 2, we compared the result of segmentations for two different sizes and positions of contour for the same picture and same iterations. The proposed model performs well and handles high non-uniformity without degrading the final segmentation results.

Bottom Line: The energy function was reduced by about 5 and 7% after 70 and 430 iterations, respectively.These results showed that, with increasing iterations, the energy function decreased, but it decreased faster during the early iterations, after which it decreased slowly.An active contour model based on the energy function is a useful tool for medical image segmentation.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

ABSTRACT

Introduction: Brain image segmentation is one of the most important clinical tools used in radiology and radiotherapy. But accurate segmentation is a very difficult task because these images mostly contain noise, inhomogeneities, and sometimes aberrations. The purpose of this study was to introduce a novel, locally statistical active contour model (ACM) for magnetic resonance image segmentation in the presence of intense inhomogeneity with the ability to determine the position of contour and energy diagram.

Methods: A Gaussian distribution model with different means and variances was used for inhomogeneity, and a moving window was used to map the original image into another domain in which the intensity distributions of inhomogeneous objects were still Gaussian but were better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field by the original signal within the window. Then, a statistical energy function is defined for each local region. Also, to evaluate the performance of our method, experiments were conducted on MR images of the brain for segment tumors or normal tissue as visualization and energy functions.

Results: In the proposed method, we were able to determine the size and position of the initial contour and to count iterations to have a better segmentation. The energy function for 20 to 430 iterations was calculated. The energy function was reduced by about 5 and 7% after 70 and 430 iterations, respectively. These results showed that, with increasing iterations, the energy function decreased, but it decreased faster during the early iterations, after which it decreased slowly. Also, this method enables us to stop the segmentation based on the threshold that we define for the energy equation.

Conclusion: An active contour model based on the energy function is a useful tool for medical image segmentation. The proposed method combined the information about neighboring pixels that belonged to the same class, thereby making it strong to separate the desired objects from the background.

No MeSH data available.


Related in: MedlinePlus