Adaptive Medical Image Deep Color Perception Algorithm

Abstract

The existing medical imaging technologies have little consideration on color information, thus most of medical images are gray. Classical hand-craft features-based methods have obtained unsatisfactory results in colorizing medical images. Moreover, these methods ignore the deep feature of medical images that represent pathology and color information. In this paper, we propose a novel method that iteratively colorizes grayscale medical images under preserving content in fine-tuned deep neural network. To the best of our knowledge, there is no currently work that attempts to colorize the medical image by using deep neural network. Specifically, we propose Y-loss which is defined as nonlinear combination of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm to preserve content invariance between target and colorized medical image. Then, adaptive reference image search algorithm is introduced to code reference and target medical image with D-hash and search reference image in hash code automatically, which free the manual selection of the reference image. Extensive experiment results show that the proposed method can generate higher quality colored medical image than recent state-of-the-art methods, and can be approved by the doctor. The objective evaluation (PSNR and SSIM) outperform an average increment 24% and 47% than baseline method, respectively. Our code is available at: https://github.com/Tongshiyue/Adaptive-medical-image-deep-color-perception-algorithm.