A Combined Method Of Fractal And GLCM Features For MRI And CT Scan Images Classification
This work addresses the need for improved clinical diagnostics for osteoporosis pathologies, but it is incremental as it combines existing methods.
The paper tackled the problem of classifying medical texture from MRI and CT scan images of trabecular bone by combining fractal features based on Box Counting with GLCM features, resulting in good classification results.
Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale complexity. The fractal feature is a compact descriptor used to give a numerical measure of the degree of irregularity of the medical images. This descriptor property does not give ownership of the local image structure. In this paper, we present a combination of this parameter based on Box Counting with GLCM Features. This powerful combination has proved good results especially in classification of medical texture from MRI and CT Scan images of trabecular bone. This method has the potential to improve clinical diagnostics tests for osteoporosis pathologies.