Texture features are: homogeneity (Hom), contrast (Ctr), entropy (Ent), standard deviation (SD), and correlation (Cor) generated from gray-level co-occurrence matrix (GLC M.
These texture features characterized homogeneity, grey-level transitions, and anatomical structures.
Thus, 4 GLCMs are obtained and by calculating the contrast, correlation, energy, and homogeneity features, a 16-dimensional feature vector is constructed for each sub-image.
After normalizing the GLCM, the contrast, correlation, energy, and homogeneity features are calculated.
Homogeneity features of dynamic systems are known to provide for a number of general practically important features.
It is demonstrated that homogeneity features significantly simplify the design and investigation of a new family of high-order sliding-mode controllers.
In this method, a co-occurrence matrix and features of energy, homogeneity, entropy and brightness are applied as feature vectors and classification is done by using the support vector machine too.
This is the case for retinotopically arranged neuronal sets that code for homogeneity features (brightness, colour, texture, etc), oriented contours, and corners of an object.
For features that violated homogeneity of variance on Levene's test of equality of error variance, a more stringent α level was set at 0.01 (cf. [ 29]).
For differentiating between all malignant and benign lesions, the following three texture features were selected: homogeneity, grey-level max probability and grey-level sum average, which achieved an AUC of 0.81.
