Texture-based feature extraction using the wavelet transform on x rays

Focal bone lesions and bone tumors are of special interest in radiology because of their rare appearance (only one percent of all tumor diseases). This motivates a computer-assisted diagnosis recognizing bone tumors. Our image analysis extracts the radiomorphologic features in x rays using a texture-based approach. Diagnosing x rays, the radiologist examines regions of different size in x rays to gain both local and global impressions of the morphologic structure. In order to analyze the x ray in different resolutions, a multiresolution approach based on the wavelet transform is applied to the radiographs. To measure the informational content of the wavelet coefficients for the individual morphologic structures, we calculated a normalized summation of the absolute wavelet coefficients within a local N by N window and called this feature the local energy. We proved in different tests this feature and the parameter for calculating the wavelet transform for a correct classification of the medical structures, applying a topologic map from Kohonen. It is shown that the wavelet transform is well suited for the feature extraction of textures.