提出G-CLAHE方法，提升医用X光图像对比度与诊断准确性
Medical X-Ray Image Enhancement Using Global Contrast-Limited Adaptive Histogram Equalization
- 融合全局与局部直方图均衡，兼顾整体与细节特征
- 实验表明可显著提升现有算法在X光图像上的增强效果
- 适合医学影像分析、放射科诊断等临床场景使用
在医学成像中，准确诊断严重依赖有效的图像增强技术，尤其针对X射线图像。现有方法常面临牺牲全局特征或局部特征的困境。本文提出一种新方法G-CLAHE（全局对比度受限自适应直方图均衡），结合全局直方图均衡（GHE）与对比度受限自适应直方图均衡（CLAHE）的优点，克服各自弱点，有效保留图像的局部与全局特性。实验结果表明，该方法能显著改进当前最先进的算法，有效提升X射线图像的对比度与质量，增强诊断准确性。
原文摘要 · Abstract (English)
In medical imaging, accurate diagnosis heavily relies on effective image enhancement techniques, particularly for X-ray images. Existing methods often suffer from various challenges such as sacrificing global image characteristics over local image characteristics or vice versa. In this paper, we present a novel approach, called G-CLAHE (Global-Contrast Limited Adaptive Histogram Equalization), which perfectly suits medical imaging with a focus on X-rays. This method adapts from Global Histogram Equalization (GHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to take both advantages and avoid weakness to preserve local and global characteristics. Experimental results show that it can significantly improve current state-of-the-art algorithms to effectively address their limitations and enhance the contrast and quality of X-ray images for diagnostic accuracy.
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