Information theory based medical images processing

Increasing application of non-invasive medical techniques (like stereotactic radiosurgery) generates a high demand for modern image processing algorithms. Image registration and segmentation are the two essential examples of this. The algorithms need to be reasonably fast, reliable, accurate, and highly automated. Information theory provides a means to create such systems. In this paper we present thresholding segmentation using image entropy and a registration technique based on maximization of mutual information. Then we show some experimental results using real-world computed tomography (CT) and medical resonance imaging (MRI) data.