Figure 5. Magnetic resonance image with eight regions of interest (ROIs) marked with different colors. ps1, ps2, ps3, foam 1, foam 2, and foam 3 are test objects. The experiment was performed in
three parts. First, higherorder features were considered only. Those were co-occurrence matrix, run-length matrix, gradient, and autoregressive model-derived parameters. The best of these were automatically selected by MaZda. Using the Bll program, the two sets of best, features were Olaparib IC50 transformed (PCA and LDA) and the transform #selleck chemical Sorafenib keyword# data were used as new features for classification (by means of a 1-NN classifier tested using the “leave-one-out” technique). The results are shown in Table I, which indicates, that lowest error figure (3/56) Inhibitors,research,lifescience,medical was obtained for the LDA data, with no possibility of perfect classification. In the second part of the experiment, histogram-based features were added to the higher-order ones used in the first part. Table I shows significance of these parameters
in region discrimination. Perfect classification was achieved for LDA- transformed data. One can notice that even if histogram data do not represent texture, they are significant to ROI classification. In the third part, wavelet-based features only were used. Table I shows that perfect ROI discrimination is possible even in the raw data space. This family of features seems to describe texture for classification purposes Inhibitors,research,lifescience,medical extremely well. The results collected (Table I) indicate that one cannot specify in advance which
particular texture features Inhibitors,research,lifescience,medical will be useful for discrimination of texture classes, and that raw-data texture features usually do not allow perfect discrimination – some pre-processing is necessary, eg, by means of linear or nonlinear discriminant transforms. Table I Number of classification errors (out of 56 samples) for higher-order features (histogram and wavelet-based features excluded; wavelet-based features excludes; and wavelet-based features only). POE, probability of classification Inhibitors,research,lifescience,medical error; AC, average correlation … Summary Texture analysis applied to MRI (and other modalities) is one of the methods that provide quantitative information about internal structure of physical objects (eg, human body tissue) visualized in images. TTiis information can be used to enhance medical diagnosis by making it more accurate and objective. Within the framework of a European COST B11 Cilengitide action, a unique package of computer programs has been developed for texture quantitative analysis in digital images. The package consists of two modules: MaZda.exe and B11.exe.The modules are seamlessly integrated, and each of the modules can be run as a separate application. Using the package, one can compute a large variety of different texture features and use them for classification of regions in the image. Moreover, MaZda allows generation of feature map images that can be used for visual analysis of image content in a new feature space, highlighting some image properties.