Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Brain Tumor Detection Using Color-Based K-Means Clustering Segmentation
IIH-MSP '07 Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) - Volume 02
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A study of efficiency and accuracy in the transformation from RGB to CIELAB color space
IEEE Transactions on Image Processing
Journal of Medical Systems
Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine
Journal of Medical Systems
Colorization of CT images to improve tissue contrast for tumor segmentation
Computers in Biology and Medicine
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
International Journal of Approximate Reasoning
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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Color segmentation of infrared thermal images is an important factor in detecting the tumor region. The cancerous tissue with angiogenesis and inflammation emits temperature pattern different from the healthy one. In this paper, two color segmentation techniques, K-means and fuzzy c-means for color segmentation of infrared (IR) breast images are modeled and compared. Using the K-means algorithm in Matlab, some empty clusters may appear in the results. Fuzzy c-means is preferred because the fuzzy nature of IR breast images helps the fuzzy c-means segmentation to provide more accurate results with no empty cluster. Since breasts with malignant tumors have higher temperature than healthy breasts and even breasts with benign tumors, in this study, we look for detecting the hottest regions of abnormal breasts which are the suspected regions. The effect of IR camera sensitivity on the number of clusters in segmentation is also investigated. When the camera is ultra sensitive the number of clusters being considered may be increased.