Abstract
Nowadays, the numerical segmentation is an important step in the processing and interpretation of medical images. The segmentation consists in extracting, from the image, one or more objects forming the regions of interest. Image thresholding is one of the simplest and effective techniques of image segmentation. In this work, we propose and compare multilevel segmentation approaches based on classical and quantum techniques. The Classical Renyi (CR) and the Quantum Renyi (QR) entropies are used to quantify the information contained in the image. Within the quantum framework, the digital image is expressed as a quantum system by means of the Flexible Representation of Quantum Images (FRQI). Generally, the multilevel thresholding formulation leads to a complex optimization problem. The Classical Genetic Algorithm (CGA) and the Quantum Genetic Algorithm (QGA) are employed to efficiently determine the optimal thresholding values by maximizing the entropy-based fitness functions. The segmentation performances of the proposed methods are assessed and compared using some prevailing criteria. The achieved results on a sample of medical images demonstrated that the QGA-QR method outperforms significantly the other combinations for this thresholding exercise.