NMR image segmentation based on Unsupervised Extreme Learning Machine

Junchang Xin, Zhongyang Wang, Shuo Tian, Zhiqiong Wang

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.