Abstract
Unsupervised Extreme Learning Machine (US-ELM) is a machine learning method widely used. With good performance in anti-noise and data representation, as well as fast clustering speed, US-ELM is suitable for processing noise containing nuclear magnetic resonance (NMR) image. Therefore, in this paper, a brain NMR image segmentation approach based on US-ELM is proposed. Firstly, a median filter is adopted to reduce the influence of noise; Secondly, US-ELM maps the original data into the embedded space, which makes it increasingly effective to represent the characteristic of pixel points, and then uses the k-means method to perform the image segmentation, named NS-UE; After that, spatial fuzzy C-means (spFCM) provides a better solution for handling NMR image with noise caused by the intensity inhomogeneity than k-means does. As a result, an image segmentation approach based on US-ELM and spFCM (NS-UF) is proposed, so as to improve the effect of clustering in embedded space. Finally, extensive experiments on real data demonstrated the efficiency and effectiveness of our proposed approaches with various experimental settings.
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This research was partially supported by the National Natural Science Foundation of China under Grant Nos. 61472069 and 61402089; And the Fundamental Research Funds for the Central Universities under Grant No. N150408001.
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Xin, J., Wang, Z., Tian, S. et al. NMR image segmentation based on Unsupervised Extreme Learning Machine. Multidim Syst Sign Process 28, 1013–1030 (2017). https://doi.org/10.1007/s11045-016-0411-6
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DOI: https://doi.org/10.1007/s11045-016-0411-6