Medical image edge detection in the framework of quantum representations

Abstract

The utilization of automated techniques for processing and analyzing medical images can offer significant support to doctors in their diagnostic and therapeutic practices. This study focuses on the complex task of detecting edges in medical images. To this end, we propose novel multilevel approaches based on quantum image representations of the one-dimensional and two-dimensional histograms of the gray-level distributions. The quantum Rényi entropy is employed to quantify the quantum information present in the two histogram types. The particle swarm optimization algorithm is utilized to identify the optimal threshold values. The performance of the proposed methods is evaluated by comparing them to benchmark methods using a set of medical images. The numerical results substantiate the efficacy of the introduced approaches.