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 ...
Fast and Robust Fuzzy C-Means Clustering Algorithms Incorporating Local Information for Image Segmentation Segmentation of medical images using adaptive region growing Adaptive Control of Robot Manipulator Using Fuzzy Compensator method comparison and bias estimation using patient samples EP09-A2 Adaptive Segme...
Similarity digest hash Dexofuzzy N-gram Clustering 1. Introduction Recent research on Android malware categorization and detection is increasingly directed toward proposing different learned models built based on various features of Android apps and machine learning algorithms. These machine learning-based st...
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) 138-162.YANG M S, TIAN Y C. Bias-correction fuzzy clustering al- gorithms [J]. Information science,...
The existing bias field correction methods do not consider the spatial information. 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...
So that, Fuzzy C-means is an overlapping clustering algorithm. The advantages of the method are its clarity, efficiency, and self-organization. It is used as beginning process in many other algorithms. The experimental evaluation of fuzzy clusyering and FCM, with bias field estimation is ...
In this research paper, gives a method bias field estimation based fuzzy clustering technique. Scan corrupted and salt-and-paper noise using Bias field estimation. Easy and simple to classify a given medical image database over a certain number of cluster fixed a-priori technique. In this ...
In order to enhance the robustness to noise, uneven illumination and other outliers, a kernel metric is introduced into fuzzy clustering method embedded with bias field correction for image segmentation. The proposed method has been compared to other state-of-the-art segmentation algorithms on both ...
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 ...