NMR image is often used in medical diagnosis. And image segmentation is one of the most important steps in the NMR image analysis, which is valuable for the computer-aided detection (CADe) and computer-aided diagnosis (CADx). As traditional image segmentation methods based on supervised learning required a lot of manual intervention. Thus, segmentation methods based on unsupervised learning have been received much concern, and unsupervised extreme learning machine (US-ELM)'s performance is particularly outstanding among the unsupervised learning methods. Therefore, in this paper, we proposed a NMR image segmentation method based on US-ELM, named NS-UE. Firstly, a NMR image feature model is established for the input NMR image; Secondly, the clustering based on US-ELM is proposed to separate the various regions of NMR image. Finally, a large number of experimental evaluation results demonstrated the effectiveness and efficiency of the proposed algorithms for the NMR image segmentation.
CITATION STYLE
Xin, J., Wang, Z., Tian, S., & Wang, Z. (2016). NMR Image Segmentation Based on Unsupervised Extreme Learning Machine (pp. 333–346). https://doi.org/10.1007/978-3-319-28397-5_26
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