The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Blur Insensitive Texture Classification Using Local Phase Quantization
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Computers in Biology and Medicine
Dominant local binary patterns for texture classification
IEEE Transactions on Image Processing
Enhanced local texture feature sets for face recognition under difficult lighting conditions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
False positive reduction in mammographic mass detection using local binary patterns
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Expert Systems with Applications: An International Journal
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In this paper, we compare different state-of-the-art texture descriptors to discriminate tissues in mammograms as either benign or malignant. The three best approaches are the following: (1)A very recent Local Ternary Pattern (LTP) variant based on a random subspace of rotation invariant bins with higher variance, where features are transformed using Neighborhood Preserving Embedding (NPE) and then used to train a support vector machine (SVM). The set of SVMs is combined by sum rule. (2)An ensemble of local phase quantization (LPQ) texture descriptors each obtained varying the parameters of LPQ. For each descriptor a SVM is trained then the SVMs are combined by sum rule. (3)A method that uses all the uniform bins extracted by LTP for training a random subspace of SVMs. The use of these techniques is very promising when applied to the task of distinguishing benign and malignant breast tissues, with the best approach being to use all the uniform bins extracted by LTP. It obtains an area under the ROC curve (AUC) of 0.97.