Esophageal endoscopic image enhancement method without reference samples
Objective Esophageal cancer is one of the most common malignant tumors that seriously threaten human health.At present,endoscopy combined with histopathological biopsy is the"gold standard"for diagnosing early esopha-geal cancer.Among them,Lugol's chromo endoscopy(LCE)has a unique advantage in gastroenterology because of its good lesion visibility,diagnostic accuracy,and low cost.However,with the rising number of patients,the imbalance between the number of doctors and patients is becoming increasingly serious.The manual diagnosis process based on endo-scopic images is susceptible to several factors,such as the experience and mental state of the doctor,the limitation of diag-nosis time,the enormous image base,and the complex and variable appearance of the lesion.Therefore,the clinical diag-nosis of artificial esophageal lesions still has a high rate of missed diagnoses and misdiagnosis.In recent years,the applica-tion of artificial intelligence(AI)in the field of medical imaging has provided strong support for doctors,and the AI-assisted diagnosis system based on deep learning can assist doctors to accurately diagnose the location and type of lesions,reducing their burden.However,deep learning models need sufficient and high-quality data.For esophageal endoscopic images,LCE esophageal endoscopic images will inevitably be affected by the built-in light source of the acquisition device during the acquisition process.The light distribution of LCE esophageal endoscopic images will be uneven due to the lim-ited illumination direction and angle of the built-in light source,affecting the overall quality of the images,which is unfa-vorable to the subsequent training of the intelligent lesion detection model.The existing low-light image enhancement algo-rithms are not ideal for the enhancement of LCE esophageal endoscopic images due to the special nature of LCE esophageal endoscopic images,complex illumination,color sensitivity,and lack of high-quality reference(paired or unpaired)datasets.Method Based on the"generative"decomposition strategy of the RetinexDIP algorithm,instead of the Retinex model,this paper uses convolutional neural networks to generate image illumination and reflection components to decompose images and proposes a stable generating network(SgNet)to solve the aforementioned problem.The encoder-decoder structure is adopted in this network.The channel attention adjustment module proposed in this paper is used to adjust the feature graphs with the same number of channels in the encoder-decoder process to ensure that the corresponding feature channel weights remain consistent.This module aims to reduce the influence of irrelevant or redundant feature channels,minimize noise interference to the network,enhance the stability of the generated network,and improve the quality of the generated image.Simultaneously,a new color model,"fixed proportion light"(FPL),which independently represents the brightness and color proportion information of the image,is proposed,and the entire light enhancement process of the image is only adjusted on the brightness channel.Thus,the overall color information of LCE esophageal endoscopic images is not disor-dered.Result The effectiveness of the proposed algorithm is tested on the self-built LCE low-light image dataset,and the visual effect and objective index evaluation are compared with numerous mainstream low-light image enhancement algo-rithms.Two methods of quality assessment without reference images were used:the natural image quality evaluator(NIQE)and the blind/referenceless image spatial quality evaluator(BRISQUE).NIQE estimates image quality by measur-ing the deviation between the natural image and statistical law,which is more in line with human subjective quality evalua-tion.Meanwhile,the BRISQUE index can measure the degree of image distortion and estimate the quality score of the image from the brightness,contrast,sharpness,color saturation,and other factors.From the comparison results of visual effects,the proposed algorithm has advantages in color fidelity,contrast enhancement,and noise reduction.Meanwhile,from the comparison results of objective indicators,the proposed algorithm ranks first in the NIQE index and second only to the GCP algorithm in the BRISQUE index.Overall,the algorithm proposed in this paper has certain advantages in visual effect and objective index.In addition,tests on four publicly available low-light image datasets,including DICM,Fusion,LIME,and NPE,as well as the publicly available low-light endoscopic image dataset Endo4IE,show that the proposed algorithm has good performance on different datasets,especially for the complex low-light characteristics of endoscopic images.Conclusion The SgNet network proposed in this paper effectively utilizes the feature channel weight information in the encoder-decoder process to improve the quality of the generated image.The illumination and reflection components of the image can be effectively generated without the need for a low-light-normal-light image pair.The proposed FPL color model can effectively ensure that the overall color information of LCE esophageal endoscopic images is not disorganized dur-ing the enhancement process.According to the experimental results,the proposed algorithm not only enhances the bright-ness of LCE esophageal endoscopy images but also effectively maintains the color and texture details of the images,which can help doctors observe the lesion tissue structure and details,improve diagnostic accuracy,and provide high-quality image data for the subsequent intelligent detection of lesions.
image enhancementLugol's chromo endoscopy(LCE)Retinex modelimage generationcolor model
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