Hybrid Cost Function for Medical Image Enhancement with Optimization Algorithms

Abstract

Medical image enhancement plays a critical role in improving diagnostic accuracy and treatment planning by enhancing the visibility of key anatomical structures and pathologies. In optimization-based enhancement techniques, the cost function is a pivotal component, guiding the enhancement process by quantitatively evaluating image quality. This paper explores the design and application of cost functions specifically tailored for medical image enhancement. We focus on optimizing key factors such as contrast, sharpness, signal-to-noise ratio (SNR), and edge preservation, which are vital for enhancing medical images while maintaining diagnostic integrity. Various optimization techniques, including gradient-based methods, genetic algorithms, and deep learning approaches, are applied to minimize the cost function. The impact of domain-specific constraints, such as preserving tissue texture and avoiding artificial artifacts, is also discussed. Experimental results on modalities such as MRI, CT, and ultrasound show that well-designed cost functions lead to significant improvements in image quality, thus facilitating better clinical outcomes and aiding medical professionals in accurate diagnosis.