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A New Method to Segment the Multiple Sclerosis Lesions on Brain Magnetic Resonance Images.

Karimian A, Jafari S - J Med Signals Sens (2015 Oct-Dec)

Bottom Line: The drawbacks of this optimization method are, it does not converge to optimal maximum or minimum and furthermore, there are some voxels, which do not fit the GMM model and have to be rejected.The automatic segmentation outputs were scored by two specialists and the results show that our method has the capability to segment the MS lesions with dice similarity coefficient score of 0.82.The results showed a better performance for the proposed approach, in comparison to those of previous works with less time-consuming.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.

ABSTRACT
Automatic segmentation of multiple sclerosis (MS) lesions in brain magnetic resonance imaging (MRI) has been widely investigated in the recent years with the goal of helping MS diagnosis and patient follow-up. In this research work, Gaussian mixture model (GMM) has been used to segment the MS lesions in MRIs, including T1-weighted (T1-w), T2-w, and T2-fluid attenuation inversion recovery. Usually, GMM is optimized by using expectation-maximization (EM) algorithm. The drawbacks of this optimization method are, it does not converge to optimal maximum or minimum and furthermore, there are some voxels, which do not fit the GMM model and have to be rejected. So, GMM is time-consuming and not too much efficient. To overcome these limitations, in this research study, at the first step, GMM was applied to segment only T1-w images by using 100 various starting points when the maximum number of iterations was considered to be 50. Then segmentation results were used to calculate the parameters of the other two images. Furthermore, FAST-trimmed likelihood estimator algorithm was applied to determine which voxels should be rejected. The output result of the segmentation was classified in three classes; White and Gray matters, cerebrospinal fluid, and some rejected voxels which prone to be MS. In the next phase, MS lesions were detected by using some heuristic rules. This new method was applied on the brain MRIs of 25 patients from two hospitals. The automatic segmentation outputs were scored by two specialists and the results show that our method has the capability to segment the MS lesions with dice similarity coefficient score of 0.82. The results showed a better performance for the proposed approach, in comparison to those of previous works with less time-consuming.

No MeSH data available.


Related in: MedlinePlus

Steps of proposed algorithm on a typical image, (a) input image, (b) brain tissue classification, (c) detection of candidate lesions with the Mahalanobis distance, (d) outliers after applying intensity rules, (e) outliers after applying other heuristic rules (multiple sclerosis lesions)
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Figure 3: Steps of proposed algorithm on a typical image, (a) input image, (b) brain tissue classification, (c) detection of candidate lesions with the Mahalanobis distance, (d) outliers after applying intensity rules, (e) outliers after applying other heuristic rules (multiple sclerosis lesions)

Mentions: Finally, all remaining voxels in the outlier set were considered as MS. The output of each step of proposed algorithm on a typical image has been shown in Figure 3.


A New Method to Segment the Multiple Sclerosis Lesions on Brain Magnetic Resonance Images.

Karimian A, Jafari S - J Med Signals Sens (2015 Oct-Dec)

Steps of proposed algorithm on a typical image, (a) input image, (b) brain tissue classification, (c) detection of candidate lesions with the Mahalanobis distance, (d) outliers after applying intensity rules, (e) outliers after applying other heuristic rules (multiple sclerosis lesions)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4759840&req=5

Figure 3: Steps of proposed algorithm on a typical image, (a) input image, (b) brain tissue classification, (c) detection of candidate lesions with the Mahalanobis distance, (d) outliers after applying intensity rules, (e) outliers after applying other heuristic rules (multiple sclerosis lesions)
Mentions: Finally, all remaining voxels in the outlier set were considered as MS. The output of each step of proposed algorithm on a typical image has been shown in Figure 3.

Bottom Line: The drawbacks of this optimization method are, it does not converge to optimal maximum or minimum and furthermore, there are some voxels, which do not fit the GMM model and have to be rejected.The automatic segmentation outputs were scored by two specialists and the results show that our method has the capability to segment the MS lesions with dice similarity coefficient score of 0.82.The results showed a better performance for the proposed approach, in comparison to those of previous works with less time-consuming.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.

ABSTRACT
Automatic segmentation of multiple sclerosis (MS) lesions in brain magnetic resonance imaging (MRI) has been widely investigated in the recent years with the goal of helping MS diagnosis and patient follow-up. In this research work, Gaussian mixture model (GMM) has been used to segment the MS lesions in MRIs, including T1-weighted (T1-w), T2-w, and T2-fluid attenuation inversion recovery. Usually, GMM is optimized by using expectation-maximization (EM) algorithm. The drawbacks of this optimization method are, it does not converge to optimal maximum or minimum and furthermore, there are some voxels, which do not fit the GMM model and have to be rejected. So, GMM is time-consuming and not too much efficient. To overcome these limitations, in this research study, at the first step, GMM was applied to segment only T1-w images by using 100 various starting points when the maximum number of iterations was considered to be 50. Then segmentation results were used to calculate the parameters of the other two images. Furthermore, FAST-trimmed likelihood estimator algorithm was applied to determine which voxels should be rejected. The output result of the segmentation was classified in three classes; White and Gray matters, cerebrospinal fluid, and some rejected voxels which prone to be MS. In the next phase, MS lesions were detected by using some heuristic rules. This new method was applied on the brain MRIs of 25 patients from two hospitals. The automatic segmentation outputs were scored by two specialists and the results show that our method has the capability to segment the MS lesions with dice similarity coefficient score of 0.82. The results showed a better performance for the proposed approach, in comparison to those of previous works with less time-consuming.

No MeSH data available.


Related in: MedlinePlus