LTF-NSI: a novel local transfer function based on neighborhood similarity index for medical image enhancement

Abstract

Medical image is an essential tool used in quantitative and qualitative evaluation of different diseases. Medical imaging methods such as fluorescein angiography (FA), optical coherence tomography angiography (OCTA), computed tomography (CT), optical coherence tomography (OCT), and X-ray are used for diagnosis. These imaging modalities suffer from low contrast, which leads to deterioration in the image quality. Consequently, this causes limitation in the usage of medical images in clinical routine and hindered its potential by depriving clinicians from assessing useful information that are needed in disease monitoring, treatment, progression, and decision-making. To overcome this limitation, we propose a novel local transfer function for medical image enhancement algorithm using the pixel neighborhood constraint. The proposed algorithm uses block-wise intensity distribution to generate the regional similarity index. The regional similarity index transformed each centered pixel in the block, to generate a new similarity image. An intuitive optimization algorithm is utilized to optimize the proposed algorithm parameters. Experimentation results show that the proposed LTF-NSI performs better than the state-of-the-art methods and improves the interpretability and perception of the medical images, which can provide clinicians and computer vision program with good quantitative and qualitative information.