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Brain MRI Segmentation Using KFCM and Chan-Vese Model 被引量:1
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作者 吴一全 侯雯 吴诗婳 《Transactions of Tianjin University》 EI CAS 2011年第3期215-219,共5页
To extract region of interests (ROI) in brain magnetic resonance imaging (MRI) with more than two objects and improve the segmentation accuracy, a hybrid model of a kemel-based fuzzy c-means (KFCM) clustering al... To extract region of interests (ROI) in brain magnetic resonance imaging (MRI) with more than two objects and improve the segmentation accuracy, a hybrid model of a kemel-based fuzzy c-means (KFCM) clustering algorithm and Chan-Vese (CV) model for brain MRI segmentation is proposed. The approach consists of two succes- sive stages. Firstly, the KFCM is used to make a coarse segmentation, which achieves the automatic selection of initial contour. Then an improved CV model is utilized to subdivide the image. Fuzzy membership degree from KFCM clus- tering is incorporated into the fidelity term of the 2-phase piecewise constant CV model to obtain accurate multi-object segmentation. Experimental results show that the proposed model has advantages both in accuracy and in robustness to noise in comparison with fuzzy c-means (FCM) clustering, KFCM, and the hybrid model of FCM and CV on brain MRI segmentation. 展开更多
关键词 brain magnetic resonance imaging image segmentation kernel-based fuzzy c-means clustering ChanVese model
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Low Speed Bearing Fault Diagnosis Based on EMD-CIIT Histogram Entropy and KFCM Clustering
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作者 ZHANG Ke LIN Tianran JIN Xia 《Journal of Shanghai Jiaotong university(Science)》 EI 2019年第5期616-621,共6页
In view of weak defect signals and large acoustic emission(AE) data in low speed bearing condition monitoring, we propose a bearing fault diagnosis technique based on a combination of empirical mode decomposition(EMD)... In view of weak defect signals and large acoustic emission(AE) data in low speed bearing condition monitoring, we propose a bearing fault diagnosis technique based on a combination of empirical mode decomposition(EMD), clear iterative interval threshold(CIIT) and the kernel-based fuzzy c-means(KFCM) eigenvalue extraction. In this technique, we use EMD-CIIT and EMD to complete the noise removal and to extract the intrinsic mode functions(IMFs). Then we select the first three IMFs and calculate their histogram entropies as the main fault features. These features are used for bearing fault classification using KFCM technique. The result shows that the combined EMD-CIIT and KFCM algorithm can accurately identify various bearing faults based on AE signals acquired from a low speed bearing test rig. 展开更多
关键词 empirical mode decomposition-clear iterative interval threshold(EMD-CIIT) kernel-based fuzzy c-means(kfcm) acoustic emission(AE) signals low speed machine roller element bearing
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