A fuzzy hyperspectral classifier for automatic target recognition (ATR) systems
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Feature-based fuzzy classification for interpretation of mammograms
Fuzzy Sets and Systems
Recognizing Deviations from Normalcy for Brain Tumor Segmentation
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
A new segmentation system for brain MR images based on fuzzy techniques
Applied Soft Computing
Numerical methods for fuzzy clustering
Information Sciences: an International Journal
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
Alternating cluster estimation: a new tool for clustering and function approximation
IEEE Transactions on Fuzzy Systems
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images
Artificial Intelligence in Medicine
Effective fuzzy c-means clustering algorithms for data clustering problems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A fast fuzzy c-means algorithm for colour image segmentation
International Journal of Information and Communication Technology
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This paper presents an automatic effective fuzzy c-means segmentation method for segmenting breast cancer MRI based on standard fuzzy c-means. To introduce a new effective segmentation method, this paper introduced a novel objective function by replacing original Euclidean distance on feature space using new hyper tangent function. This paper obtains the new hyper tangent function from exited hyper tangent function to perform effectively with large number of data from more noised medical images and to have strong clusters. It derives an effective method to construct the membership matrix for objects, and it derives a robust method for updating centers from proposed novel objective function. Experiments will be done with an artificially generated data set to show how effectively the new fuzzy c-means obtain clusters, and then this work implements the proposed methods to segment the breast medical images into different regions, each corresponding to a different tissue, based on the signal enhancement-time information. This paper compares the results with results of standard fuzzy c-means algorithm. The correct classification rate of proposed fuzzy c-means segmentation method is obtained using silhouette method.