Abstract:
The diagnostic interpretation of medical images is a complex task aiming to detect potential
abnormalities. One of the most used features in this process is texture which is a key
component in the human understanding of images. Many studies were conducted to develop
algorithms for texture quantification. The relevance of fractal geometry in medical image
analysis is justified by the proven self-similarity of anatomical objects when imaged with a
finite resolution. Over the last years, fractal geometry was applied extensively in many
medical signal analysis applications. The use of these geometries relies heavily on estimation
of the fractal features. Various methods were proposed to estimate the fractal dimension. This
article presents an overview of these algorithms, the way they work, their benefits and limits,
and the ability to differentiate osteoporosic groups from control groups. Their clinical
potential appears very promising.
