This paper shows a complete and novel study for breast TMA classification based on texture, frequential features and color models information. Thus a relevant set of features for automatic breast TMA classification is found. These features are obtained from 1st and 2nd order Haralick statistical descriptors as well as by filtering with the Fourier and Daubechies Wavelet transforms. Moreover, the discriminant value of the features has been analyzed under different color models, that is, RGB, Lab*, HSV, Hb* and b* channel. Thus, a total of 3133 features with 5 color models were analyzed. The TMA images were divided into four classes: i) benign stromal tissue with cellularity, ii) adipose tissue, iii) benign structures but anomalous and iv) ductal and lobular carcinomas. A statistical study of the features was carried out to provide information about their influence in the tissue classes. Finally, the classification was performed with 10 classifiers using 10-fold and leave-one-out cross validation for all color models selected. Furthermore, the classification results were also analyzed by means of a principal component analysis (PCA). A 95% accuracy and 90% precision with PCA and a Fisher classifier on Hb* image channels is obtained with the selected features.
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- Breast Tissue Microarray Classification Based on Texture and Frequential Features
M. M. Fernández-Carrobles