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Year : 2019  |  Volume : 5  |  Issue : 1  |  Page : 37-45

Level set evolution with intensity prior knowledge for multiple sclerosis lesion segmentation

1 Department of Computer, School of Computer, Shenyang Aerospace University, Shenyang, Liaoning Province, China
2 Department of Radiology, The General Hospital of Shenyang Military, Shenyang, Liaoning Province, China
3 Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, Liaoning Province, China

Correspondence Address:
Guodong Zhang
School of Computer, Shenyang Aerospace University, Shenyang 110136, Liaoning Province
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/digm.digm_5_19

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Background and Objectives: Multiple sclerosis (MS) lesion segmentation is important in estimating the progress of the disease and measuring the impact of new clinical treatments. Manual lesion delineation for the segmentation of lesions is time-consuming and suffers from observer variability. Therefore, a fully automated MS lesion segmentation method is considerable important in clinical practice. Subjects and Methods: In this study, we present a multilabel fusion embedded level set method for white matter lesion segmentation from MS patient images. Specifically, we focus on the validation of the variational level set method. Lesion segmentation is achieved by extending the level set contour which consists of an intensity-constrained term, an image data term, and a regularization term. Results: To compare the performance of our method with other state-of-the-art methods, we evaluated the methods with 25 magnetic resonance imaging datasets of MS patients. The dice score reaches an average of 0.55 for the proposed method. The sensitivity value and specificity value reach an average of 0.89 and 0.14, respectively. Conclusions: Experimental results demonstrate that our method is robust to parameter setting and outperforms other methods. The intensity-constrained term plays a key role in improving the segmentation accuracy. The experimental results show that our approach is effective and robust for lesion segmentation, which might simplify the quantification of lesions in basic research and even clinical trials.

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