EIEN: Endoscopic Image Enhancement Network Based on Retinex Theory

An, Ziheng; Chen, Xu; Qian, Kai; Han, Jubao; Tan, Wei; Wang, Dou; Fang, Qianqian

doi:10.3390/s22145464

Cited by 11 publications

(4 citation statements)

References 33 publications

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“…In this paper, U-Net is chosen as the basic model for leukocyte segmentation because U-net can get a good training effect using fewer data sets, the problem of small number of leukocyte datasets has been solved. The data enhancement preprocessing part first converts the RGB color space of the cell images into HSV (hue, saturation, value) color space and then introduces the AHE-Retinex method based on the combination of HE 13 and retinex theory 14 of the OpenCV platform to improve the generalization ability of the network. The processed images are passed into a contracting path with VGG16 as the backbone for feature extraction.…”

Section: Methodsmentioning

confidence: 99%

Improved U-net-based leukocyte segmentation method

et al. 2023

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. Significance Leukocytes are mainly composed of neutrophils, basophils, eosinophils, monocytes, and lymphocytes. The number and proportion of different types of leukocytes correspond to different diseases, so an accurate segmentation of each type of leukocyte is important for the diagnosis of disease. However, the acquisition of blood cell images can be affected by external environmental factors, which can lead to variable light and darkness, complex backgrounds, and poorly characterized leukocytes. Aim To address the problem of complex blood cell images collected under different environments and the lack of obvious leukocyte features, a leukocyte segmentation method based on improved U-net is proposed. Approach First, adaptive histogram equalization-retinex correction was introduced for data enhancement to make the leukocyte features in the blood cell images clearer. Then, to address the problem of similarity between different types of leukocytes, convolutional block attention module is added to the four skip connections of U-net to focus the features from spatial and channel aspects, so that the network can quickly locate the high-value information of features in different channels and spaces. It avoids the problem of large amount of repeated computation of low-value information, prevents overfitting, and improves the training efficiency and generalization ability of the network. Finally, to solve the problem of class imbalance in blood cell images and to better segment the cytoplasm of leukocytes, a loss function combining focal loss and Dice loss is proposed. Results We use the BCISC public dataset to verify the effectiveness of the proposed method. The segmentation of multiple leukocytes using the method of this paper can achieve 99.53% accuracy and 91.89% mIoU. Conclusions The experimental results show that the method achieves good segmentation results for lymphocytes, basophils, neutrophils, eosinophils, and monocytes.

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Section: Methodsmentioning

confidence: 99%

Improved U-net-based leukocyte segmentation method

et al. 2023

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“…In recent years, researchers have focused on applying deep learning networks to enhance the quality of endoscopic images. To improve the visual quality and discernibility of endoscopic images, An et al proposed a Retinex-Net network based on the Retinex theory, which employs image decomposition techniques to decompose the original endoscopic image into different frequency components, such as low-frequency and high-frequency components [36]. By enhancing the different components, the algorithm can enhance the contrast and details of the image and reduce the impact of image blur.…”

Section: Image Deblurringmentioning

confidence: 99%

Research progress of frontier image processing in medical endoscopes

Wu,

Yuan,

Liu

et al. 2023

PPM

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In the modern medical diagnosis, digital medical images can provide physicians with a more accurate, visualized, and three-dimensional view of various tissues. These images assist in predicting, diagnosing, and treating diseases. However, medical images are highly susceptible to noise contamination from the influence of imaging equipment and the capture process, which poses a significant challenge in the analysis of medical images. This review summarizes the image processing technologies applied in endoscopy, such as image denoising, image deblurring, image enhancement, and image segmentation, involving traditional computational models and deep learning algorithms used in these technologies. Additionally, the clinical applications of these techniques are also discussed.

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“…Enhancement was introduced into a deep neural network consisting of multiple alternating enhancement modules and reverse projection modules. In the endoscopic image enhancement network, researchers used transfer learning to train a decomposed network model based on retinex theory and proposed a self-attention guided multi-scale pyramid network to obtain a satisfactory illumination component [44]. In contrast to fully supervised learning frameworks, some approaches apply unsupervised learning techniques to train neural network models without explicit labeling of training pairs.…”

Section: Related Workmentioning

confidence: 99%

Cervical Precancerous Lesion Image Enhancement Based on Retinex and Histogram Equalization

Ren,

Li,

2023

Mathematics

Self Cite

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Cervical cancer is a prevalent chronic malignant tumor in gynecology, necessitating high-quality images of cervical precancerous lesions to enhance detection rates. Addressing the challenges of low contrast, uneven illumination, and indistinct lesion details in such images, this paper proposes an enhancement algorithm based on retinex and histogram equalization. First, the algorithm solves the color deviation problem by modifying the quantization formula of retinex theory. Then, the contrast-limited adaptive histogram equalization algorithm is selectively conducted on blue and green channels to avoid the problem of image visual quality reduction caused by drastic darkening of local dark areas. Next, a multi-scale detail enhancement algorithm is used to further sharpen the details. Finally, the problem of noise amplification and image distortion in the process of enhancement is alleviated by dynamic weighted fusion. The experimental results confirm the effectiveness of the proposed algorithm in optimizing brightness, enhancing contrast, sharpening details, and suppressing noise in cervical precancerous lesion images. The proposed algorithm has shown superior performance compared to other traditional methods based on objective indicators such as peak signal-to-noise ratio, detail-variance–background-variance, gray square mean deviation, contrast improvement index, and enhancement quality index.

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EIEN: Endoscopic Image Enhancement Network Based on Retinex Theory

Cited by 11 publications

References 33 publications

Improved U-net-based leukocyte segmentation method

Improved U-net-based leukocyte segmentation method

Research progress of frontier image processing in medical endoscopes

Cervical Precancerous Lesion Image Enhancement Based on Retinex and Histogram Equalization

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