We also applied the bias-correction fuzzy clustering algorithms to real data sets. The results indicated the superiority and effectiveness of the proposed bias-correction fuzzy clustering methods.doi:10.1016/j.ins.2015.03.006Yang, Miin-ShenDepartment of Applied Mathematics, Chung Yuan Christian ...
Fuzzy clusteringInitializationProbability weightYangMiin-ShenTianYi-ChengInformation SciencesM.S. Yang, Y.C. Tian, Bias-correction fuzzy clustering algorithms, Inf. Sci. 309 (2015) (2015) 138-162.S.Y. Miin, C.T. Yi, Bias-correction fuzzy clustering algorithms, Inf. Sci. 309 (10) (2015) ...
Further, the problem of equidistant pixels while clustering is not addressed. These problems lead to poor segmentation accuracy. To solve these problems, the authors suggest a novel biased fuzzy clustering technique for the problem on hand. The basic idea is to incorporate the spatial information ...
In this paper, we present a Gaussian kernel-based fuzzy c-means algorithm (GKFCM) with a spatial bias correction. The proposed GKFCM algorithm becomes a generalized type of FCM, BCFCM, KFCM_S 1 and KFCM_S 2 algorithms and presents with more efficiency and robustness. Some numerical and ...
The existing solutions used fuzzy c-means (FCM) algorithm with non-local spatial information. However, the use of only local spatial information may lead to poor segmentation of tissue regions. In this paper, we suggest a nonlocal spatial coherent FCM clustering scheme for bias field correction....
This paper improves the multi-scale Gaussian kernel induced fuzzy C-means clustering method with spatial bias correction (MsGKFCM_S). Furthermore, it presents a hybrid segmentation method, using both the features of the MsGKFCM_S clustering and active contour driven by a region-scalable fitting ...