Adaptive Hybrid CNN–Wavelet Framework for Low-Light Medical Image Enhancement and Diagnosis Support

Abstract

Images of medical patients that were taken in low light or low contrast areas frequently have issues with noise, visibility, and important information for physicians to use in deciding on patient care. An Adaptive Hybrid Convolutional Neural Network Discrete Wavelet Transform Enhancement Technique for Low Light Medical Images is presented for use in helping to detect latent disease in X-ray and Magnetic Resonance Images. The method works as follows: The input image first goes through a Discrete Wavelet Transform to break it down into high frequency and low frequency components. Separating out the high frequency (noise) and low frequency parts of the image allows for a more efficient way to reduce noise while still preserving the critical structure of the image. Once the first step has been completed, multi-scale wavelet features are used to create a Convolutional Neural Network (CNN) enhancement module that learns how to adaptively learn how to make illumination corrections and improve contrast. The final part of the process is an Adaptive Histogram Equalization postprocessing step that improves visual clarity, therefore enhancing the final image to allow for good clinical interpretation. There are experimental results that demonstrate the new proposed framework is significantly better than existing methods on several common image quality metrics such as PERMANENT CRYSTAL, PSNR, and SSIM, and that it preserves the critical diagnostic features of the medical image. This method is extremely beneficial to radiologists because it allows for the accurate and reliable analysis of MRI images using Computer Assisted Diagnosis (CAD) systems and can be integrated with existing CAD systems to enhance and improve the radiologist’s ability to interpret the medical images of their patients.