用智能优化算法提升宫颈涂片图像清晰度，助力癌症早期检测。
Optimized Pap Smear Image Enhancement: Hybrid PMD Filter-CLAHE Using Spider Monkey Optimization
- 结合PMD去噪与CLAHE增强，通过蛛猴优化算法协同调参。
- 在SIPaKMeD数据集上，对比度提升至60.45，熵达6.80，效果显著。
- 适合医学图像处理、病理影像分析领域的研究人员参考。
宫颈涂片图像质量对宫颈癌检测至关重要。本文提出一种优化的混合增强方法，结合了Perona-Malik扩散（PMD）滤波器与对比度受限自适应直方图均衡化（CLAHE），以提升图像质量。PMD滤波器用于降噪，而CLAHE则增强对比度。该混合方法通过蛛猴优化算法（SMO PMD-CLAHE）进行优化，分别采用BRISQUE和CEIQ作为PMD滤波器与CLAHE的客观评价函数。实验基于SIPaKMeD数据集进行。结果表明，SMO在优化PMD与CLAHE方面优于现有先进方法。所提方法平均有效增强度（EME）达5.45，均方根对比度（RMS）为60.45，迈克尔逊对比度（MC）为0.995，熵值为6.80，显著提升了图像质量，为宫颈涂片图像增强提供了新思路。
原文摘要 · Abstract (English)
Pap smear image quality is crucial for cervical cancer detection. This study introduces an optimized hybrid approach that combines the Perona-Malik Diffusion (PMD) filter with contrast-limited adaptive histogram equalization (CLAHE) to enhance Pap smear image quality. The PMD filter reduces the image noise, whereas CLAHE improves the image contrast. The hybrid method was optimized using spider monkey optimization (SMO PMD-CLAHE). BRISQUE and CEIQ are the new objective functions for the PMD filter and CLAHE optimization, respectively. The simulations were conducted using the SIPaKMeD dataset. The results indicate that SMO outperforms state-of-the-art methods in optimizing the PMD filter and CLAHE. The proposed method achieved an average effective measure of enhancement (EME) of 5.45, root mean square (RMS) contrast of 60.45, Michelson's contrast (MC) of 0.995, and entropy of 6.80. This approach offers a new perspective for improving Pap smear image quality.
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