Cervical Precancerous Lesion Image Enhancement Based on Retinex and Histogram Equalization

Yuan Ren, Zhengping Li () and Chao Xu
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Yuan Ren: School of Integrated Circuits, Anhui University, Hefei 230601, China
Zhengping Li: School of Integrated Circuits, Anhui University, Hefei 230601, China
Chao Xu: School of Integrated Circuits, Anhui University, Hefei 230601, China

Mathematics, 2023, vol. 11, issue 17, 1-21

Abstract: 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.

Keywords: image enhancement; cervical precancerous lesion; retinex; histogram equalization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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