Local fractal and multifractal features for volumic texture characterization

For texture analysis, several features such as co-occurrence matrices, Gabor filters and the wavelet transform are used. Recently, fractal geometry appeared to be an effective feature to analyze texture. But it is often restricted to 2D images, while 3D information can be very important especially in medical image processing. Moreover applications are limited to the use of fractal dimension. This study focuses on the benefits of fractal geometry in a classification method based on volumic texture analysis. The proposed methods make use of fractal and multifractal features for a 3D texture analysis of a voxel neighborhood. They are validated with synthetic data before being applied on real images. Their efficiencies are proved by comparison to some other texture features in supervised classification processes (AdaBoost and support vector machine classifiers). The results showed that features based on fractal geometry (by combining fractal and multifractal features) contributed to new texture characterization. Information on new features was useful and complementary for a classification method. This study suggests that fractal geometry can provide a new useful information in 3D texture analysis, especially in medical imaging. ► Algorithms for estimating fractal and multifractal features in 3D medical images. ► Optimal parameters tuning using synthetic data. ► Definition of a general classification framework using these features. ► Validation on clinical data in 2 different applications.