The example experiment described here was made to verify whether texture classes represented in the image in
Figure 4a could be classified based on some selected texture parameters computed using the MaZda software. Figure 4. Magnetic resonance image cross-section of four test objects of different texture. B. Four {Selleck Anti-cancer Compound Library|Selleck Anticancer Compound Library|Selleck Anti-cancer Compound Library|Selleck Anticancer Compound Library|Selleckchem Anti-cancer Compound Library|Selleckchem Anticancer Compound Library|Selleckchem Anti-cancer Compound Library|Selleckchem Anticancer Compound Library|Anti-cancer Compound Library|Anticancer Compound Library|Anti-cancer Compound Library|Anticancer Compound Library|Anti-cancer Compound Library|Anticancer Compound Library|Anti-cancer Compound Library|Anticancer Compound Library|Anti-cancer Compound Library|Anticancer Compound Library|Anti-cancer Compound Library|Anticancer Compound Library|Anti-cancer Compound Library|Anticancer Compound Library|Anti-cancer Compound Library|Anticancer Compound Library|Anti-cancer Compound Library|Anticancer Compound Library|buy Anti-cancer Compound Library|Anti-cancer Compound Library ic50|Anti-cancer Compound Library price|Anti-cancer Compound Library cost|Anti-cancer Compound Library solubility dmso|Anti-cancer Compound Library purchase|Anti-cancer Compound Library manufacturer|Anti-cancer Compound Library research buy|Anti-cancer Compound Library order|Anti-cancer Compound Library mouse|Anti-cancer Compound Library chemical structure|Anti-cancer Compound Library mw|Anti-cancer Compound Library molecular weight|Anti-cancer Compound Library datasheet|Anti-cancer Compound Library supplier|Anti-cancer Compound Library in vitro|Anti-cancer Compound Library cell line|Anti-cancer Compound Library concentration|Anti-cancer Compound Library nmr|Anti-cancer Compound Library in vivo|Anti-cancer Compound Library clinical trial|Anti-cancer Compound Library cell assay|Anti-cancer Compound Library screening|Anti-cancer Compound Library high throughput|buy Anticancer Compound Library|Anticancer Compound Library ic50|Anticancer Compound Library price|Anticancer Compound Library cost|Anticancer Compound Library solubility dmso|Anticancer Compound Library purchase|Anticancer Compound Library manufacturer|Anticancer Compound Library research buy|Anticancer Compound Library order|Anticancer Compound Library chemical structure|Anticancer Compound Library datasheet|Anticancer Compound Library supplier|Anticancer Compound Library in vitro|Anticancer Compound Library cell line|Anticancer Compound Library concentration|Anticancer Compound Library clinical trial|Anticancer Compound Library cell assay|Anticancer Compound Library screening|Anticancer Compound Library high throughput|Anti-cancer Compound high throughput screening| regions of interest (four texture classes) defined for the image in A. There were 22 images showing different cross-sections of the test objects, leading to 22 examples of texture of each class. Numerical values of about. 300 Inhibitors,research,lifescience,medical texture statistical parameters were computed using MaZda module. This step produced eighty-eight 300-dimensional data vectors. A list of 10 best, features was then automatically generated based on Fisher coefficient criterion (maximization
of the ratio F of between-class to within-class variance). The best parameters were then passed to the Inhibitors,research,lifescience,medical B11 module. Thus, the Inhibitors,research,lifescience,medical input to B11 was made of eighty-eight 10-dimensional data vectors, with 22 vectors for each texture class. A scatter plot of the input data in the 3D data space was made of first three best texture features. The raw data were transformed to lower-dimensional spaces, using the PCA, LDA, and NDA projections. In each case, the Fisher coefficient F was calculated for the obtained data vectors. They were also classified using a 1-NN classifier, and tested using a leave-one-out Inhibitors,research,lifescience,medical technique.36 The PCA projection to a lower-dimensionality data space does not improve the classification accuracy. This can be explained by the fact Inhibitors,research,lifescience,medical that PCA is optimized for representation of data variability, which is not the same as data suitability for class discrimination (which is the case of LDA). Although the LDA gives lower value of the .Fisher coefficient F, it eliminates the classification errors. Thus, the lower F coefficient, does not necessarily indicate worse classification. Extremely large
F can be obtained using NDA; however, one should verify (using a separate test, dataset) whether the ANN does not suffer from the overtraining problem.38 An overtrained network does not. generalize the training data well and, consequently, it may wrongly classify Idoxuridine unseen data points. Application example 2 Figure 5 shows an MRI image that contains cross-section of human scull, along with cross-section of six artificial test objects (phantoms designed and manufactured to generate standard texture patterns), three on each side of the scull. There are altogether eight ROIs defined for this image, each marked with a different color. The numerical experiment carried out.