Abstract
Accurate segmentation of ischemic lesions is still a challenging task. In this paper, we propose a framework to extract ischemic lesions from multi-spectral MR images. In the proposed framework, MR images of each modality are first segmented into brain tissues and ischemic lesions by weighting suppressed fuzzy c-means. Preliminary lesion segmentation results are then fused among all the imaging modalities by majority voting. The fused segmentation results are finally refined by a three phase level set method. The level set formulation is defined on multi-spectral images with the capability of dealing with intensity inhomogeneities. The proposed framework has been applied to the MICCAI 2015 ISLES challenge. According to the ranking rules of the challenge, the proposed framework took the second place and the fourth place in sub-acute lesion segmentation and acute stroke estimation, respectively.
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Acknowledgement
This work was supported by the Fundamental Research Funds for the Central Universities of China under grant N140403006, N140402003, and N140407001, the Postdoctoral Scientific Research Funds of Northeastern University under grant No. 20150310, the National Science Foundation for Distinguished Young Scholars of China under Grant Nos. 71325002 and 61225012, the Chinese National Natural Science Foundation under grant Nos. 61172002 and 71071028, the National Key Technology Research and Development Program of the Ministry of Science and Technology of China under grant 2014BAI17B01, and the Fundamental Research Funds for State Key Laboratory of Synthetical Automation for Process Industries under Grant No. 2013ZCX11.
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Feng, C., Zhao, D., Huang, M. (2016). Segmentation of Ischemic Stroke Lesions in Multi-spectral MR Images Using Weighting Suppressed FCM and Three Phase Level Set. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. Lecture Notes in Computer Science(), vol 9556. Springer, Cham. https://doi.org/10.1007/978-3-319-30858-6_20
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DOI: https://doi.org/10.1007/978-3-319-30858-6_20
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