Nonlocally adaptive image enhancement system for full-field optical coherence tomography

Abstract

Full-field optical coherence tomography (FFOCT) can be used to acquire cellular-level morphological images of nonfixed and unstained biological tissues. However, the speckle noise and vignetting of FFOCT images are drawbacks in terms of pathological application. We present a novel adaptive nonlocal image enhancement algorithm that uses a fluorescence microscopy (FM) image of the nucleus to achieve cytoplasm image enhancement under FFOCT. The algorithm comprises a denoising module and an adaptive nonlocal enhancement module. The denoising module is an end-to-end residual network with skip connections and dilated convolution. The adaptive nonlocal enhancement module enhances images to segment tissues, nontissues, and transition areas so that the images retain the details of the original cell structure but do not contain noise. The proposed algorithm was implemented on a dual-modality scanner system that combines FFOCT and FM. A total of 12 pig liver and pig kidney tissues were sectioned into 10,236 samples for experiments. The peak signal-to-noise ratio and structural similarity index value increased from 30.8 and 0.58, respectively, for the aforementioned system without the proposed algorithm to 34.2 and 0.86, respectively, for the aforementioned system with the proposed algorithm. Ten samples of human breast tissues were used to validate the proposed algorithm. The average contrast-to-noise ratio was 1.69 and 11.73 when the aforementioned system was used with and without the proposed algorithm, respectively. The results of this study revealed that the quality of scanned images considerably improved when the proposed system was used and that the proposed system can facilitate clinical pathological diagnosis.