Shannon Entropy in the Automatic Identification of Microbleeds Using Image Processing

Abstract

The detection of microbleeds in Magnetic Resonance Imaging (MRI) studies through the Susceptibility Weighted Image (SWI) technique is presented. The SWI technique has shown to play a relevant role in the identification of microbleeds, unlike conventional MRI techniques, the sensitivity is higher for detecting microbleeds and iron deposits. This work presents a technique for the quantitative detection of microbleeds through the implementation of Shannon entropy. It is complemented with a statistical analysis of the results and establishes a specific range that allows for the early detection of these structures in future research. Preliminary results suggest that this method represents a significant advancement in the accurate and timely detection of cerebral microbleeds, offering an alternative to traditional Magnetic Resonance approaches, suggesting that artificial intelligence is a promising path for deeper investigations in this field.