Xray-Net: Self-supervised pixel stretching approach to improve low-contrast medical imaging

Medical imaging is crucial for monitoring the progression of a diagnosis. However, poor-quality images, particularly radiographs which are commonly used for their affordability, can lack sufficient visual information and lead to lengthy and inaccurate diagnoses, reducing clinicians’ ability to examine and decreasing the accuracy of their assumptions. To address this issue, this paper proposes an Xray-Net approach that utilizes self-supervised pixel-contrast stretching to improve low-contrast images. The approach consists of three main phases: (1) Pre-processing, which involves obtaining X-ray contrast-based classification through the measurement of average brightness, (2) Development of a conventional neural network model, and (3) Contrast-adaptive stretching, where the stretching control coefficients are predicted adaptively based on input image information. The proposed approach was evaluated using quantitative and qualitative experiments on three X-ray databases, and six different quality measures were considered. The results obtained by Xray-Net demonstrate superior low-contrast enhancement performance compared to several state-of-the-art methods. •Low-contrast radiography enhancement by adaptive intensity transformation.•Adaptive intensity transformation using pixel-stretching-based learning.•Deep model-based learning approach for self-supervised pixel stretch.•Evaluation of the proposed approach with SOTA, qualitatively and quantitatively.•Proposed approach achieves enhanced radiographs compared to related methods.