Clasificación Automática de Sujetos con Deterioro Cognitivo Leve mediante la Caracterización Fractal 3D en Imágenes Cerebrales de Resonancia Magnética

Abstract

Sixty brain volumes were analyzed from separate magnetic resonance images in two populations: healthy control subjects and subjects with mild cognitive impairment. For each element, the Box Counting algorithm was applied to obtain the characterization of the 3D Fractal Dimension that it presented. In addition, other morphological indices of volume, discrete compactness and surrounding area were added. Finally, a classification strategy is implemented using the Gaussian Process with a radial-based Kernel to compare the correct discrimination in the populations studied. The classifier model after the validation process gave a 60% success rate for the fractal dimension and for the morphological indices. The highest hit rate was when both metrics were combined with 80%. With these results, it is believed that the fractal index could become a biomarker for the prediagnosis of Alzheimer's disease, although for this, more studies are needed in the future.