A metaheuristic automated framework for quality improvement of CT imagery

Abstract

Abstract Contrast-limited adaptive histogram equalization (CLAHE) is a technique often used for enhancing the quality of computed tomography (CT) images. The quality of enhanced CT slices produced by the CLAHE depends on the selection of the clip-limit (CL). The CLAHE driven with inadequate value of CL selected via trial and error may amplify noise, mask subtle structures, and hinder the overall perceptual quality of the processed CT slices. As a solution to this problem, we introduce a metaheuristic framework to facilitate automated CL selection in CLAHE, especially for CT contrast enhancement. The framework uses the whale algorithm as optimizer and a perception-based image quality evaluator (PIQE) as fitness. On 315 CT slices, the WOA-PIQE-CLAHE framework produced outputs that have lower PIQE (29.8063 ± 1.1433) and higher contrast (71.5385 ± 1.0408) compared to the low-contrast slices (PIQE = 32.0184 ± 0.9894 & Contrast = 69.3130 ± 1.0465). Increase in contrast and decrease in PIQE manifest that the framework improves tissue contrast without hindering the perceptual quality of the CT slices.