Principles and practice of information theory
Principles and practice of information theory
Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated image analysis techniques for digital mammography
Automated image analysis techniques for digital mammography
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Information Clustering Algorithm
Neural Computation
Robust support vector machine with bullet hole image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fuzzy c-means clustering of incomplete data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust clustering by pruning outliers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Computation of channel capacity and rate-distortion functions
IEEE Transactions on Information Theory
Adaptive spatial information-theoretic clustering for image segmentation
Pattern Recognition
JPEG2000 ROI coding method with perfect fine-grain accuracy and lossless recovery
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
JPEG2000 ROI coding through component priority for digital mammography
Computer Vision and Image Understanding
An information theoretic sparse kernel algorithm for online learning
Expert Systems with Applications: An International Journal
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In this paper, we investigate a robust information clustering (RIC) algorithm incorporating spatial information for breast mass detection in digitized mammograms. The detection system employs RIC algorithm based on the raw region of interest (ROI) extracted from global mammogram by two steps of adaptive thresholding. Pixels on the fuzzy margin of a mass and noisy data were identified by RIC through the minimax optimization of mutual information. The memberships of the identified pixels (outliers) were recalculated by incorporating spatial distance information that takes into account of the influence of a neighborhood of 3x3 window. The algorithm is robust in the sense that both peak and valley of image intensity histogram are estimated and the pixels corresponding to valley in the histogram are clustered adaptively to image content. The purpose of the detection system is to locate the suspicious regions of mass candidates in the mammograms which will be further examined by other diagnostic techniques or by radiologists. The proposed method has been verified with 60 mammograms in the MiniMIAS database. The experimental results show that the detection system has a sensitivity of 90.7% at 2.57 false positives (FPs) per image.