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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

Effect of h on dice similarity coefficient factor for both groups of patients with low and high lesion load
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Figure 4: Effect of h on dice similarity coefficient factor for both groups of patients with low and high lesion load

Mentions: As it was mentioned in the previous section, h value has a great effect on the accuracy of MS lesion detection and image segmentation. The larger amount of h means, the more tissue voxels considered as MS candidate and, on the other hand, smaller value of h result means missing some MS regions. To find the optimum value of h for our database, we changed h value from 0 to 0.5 (limit of convergence) and recorded the Dice similarity factor, which was calculated by two experts. Results showed that for patient with high lesion load (≥10 cm3), optimum h is 0.25 and for low lesion load (<10 cm3) h should be in the range of 0.1–0.15. Figure 4 shows h effects on dice factor for both groups of patients with low and high lesion load.


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)

Effect of h on dice similarity coefficient factor for both groups of patients with low and high lesion load
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Effect of h on dice similarity coefficient factor for both groups of patients with low and high lesion load
Mentions: As it was mentioned in the previous section, h value has a great effect on the accuracy of MS lesion detection and image segmentation. The larger amount of h means, the more tissue voxels considered as MS candidate and, on the other hand, smaller value of h result means missing some MS regions. To find the optimum value of h for our database, we changed h value from 0 to 0.5 (limit of convergence) and recorded the Dice similarity factor, which was calculated by two experts. Results showed that for patient with high lesion load (≥10 cm3), optimum h is 0.25 and for low lesion load (<10 cm3) h should be in the range of 0.1–0.15. Figure 4 shows h effects on dice factor for both groups of patients with low and high lesion load.

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