Gangwar Shivam, Devi Reeta, Mat Isa Nor Ashidi
Department of Electronics and Communication Engineering, University Institute of Engineering & Technology, Kurukshetra University, Kurukshetra, Haryana, 136119, India.
School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Penang, 14300, Malaysia.
Sci Rep. 2025 Feb 25;15(1):6693. doi: 10.1038/s41598-025-90876-6.
In medical imaging, low-contrast chest X-ray (CXR) images may fail to provide adequate information for accurate visual interpretation and disease diagnosis. Conventional contrast enhancement techniques, such as histogram equalization, often introduce intensity shifts and loss of fine details. This study presents an advanced Exposure Region-Based Modified Adaptive Histogram Equalization (ERBMAHE) method, further optimized using Particle Swarm Optimization (PSO) to enhance contrast, preserve brightness, and strengthen fine details. The ERBMAHE method segments CXR images into underexposed, well-exposed, and overexposed regions using the 9IEC algorithm. The well-exposed region is further divided, generating five histograms. Each region undergoes adaptive contrast enhancement via a novel weighted probability density function (PDF) and power-law transformation to ensure balanced enhancement across different exposure levels. The PSO algorithm is then employed to optimize power-law parameters, further refining contrast enhancement and illumination uniformity while maintaining the natural appearance of medical images. The PSO-ERBMAHE method was tested on 600 Kaggle CXR images and compared against six state-of-the-art techniques. It achieved a superior peak signal-to-noise ratio (PSNR = 31.10 dB), entropy (7.48), feature similarity index (FSIM = 0.98), tenengrad function (TEN = 0.19), quality-aware relative contrast measure (QRCM = 0.10), and contrast ratio, while maintaining a low absolute mean brightness error (AMBE = 0.10). The method effectively enhanced image contrast while preserving brightness and visual quality, as confirmed by medical expert evaluations. The proposed PSO-ERBMAHE method delivers high-quality contrast enhancement in medical imaging, ensuring better visibility of critical anatomical features. By strengthening fine details, maintaining mean brightness, and improving computational efficiency, this technique enhances disease examination and diagnosis, reducing misinterpretation risks and improving clinical decision-making.
在医学成像中，低对比度胸部X光（CXR）图像可能无法提供足够的信息用于准确的视觉解读和疾病诊断。传统的对比度增强技术，如实直方图均衡化，常常会引入强度偏移和细节丢失。本研究提出了一种先进的基于曝光区域的改进自适应直方图均衡化（ERBMAHE）方法，并使用粒子群优化（PSO）进一步优化，以增强对比度、保持亮度并强化细节。ERBMAHE方法使用9IEC算法将CXR图像分割为曝光不足、曝光良好和曝光过度的区域。曝光良好的区域进一步划分，生成五个直方图。每个区域通过一种新颖的加权概率密度函数（PDF）和幂律变换进行自适应对比度增强，以确保在不同曝光水平上实现平衡增强。然后使用PSO算法优化幂律参数，进一步细化对比度增强和光照均匀性，同时保持医学图像的自然外观。PSO-ERBMAHE方法在600张Kaggle CXR图像上进行了测试，并与六种先进技术进行了比较。它实现了更高的峰值信噪比（PSNR = 31.10 dB）、熵（7.48）、特征相似性指数（FSIM = 0.98）、 tenengrad函数（TEN = 0.19）、质量感知相对对比度度量（QRCM = 0.10）和对比度比，同时保持较低的绝对平均亮度误差（AMBE = 0.10）。医学专家评估证实，该方法在保持亮度和视觉质量的同时有效地增强了图像对比度。所提出的PSO-ERBMAHE方法在医学成像中提供了高质量的对比度增强，确保关键解剖特征具有更好的可见性。通过强化细节、保持平均亮度并提高计算效率，该技术增强了疾病检查和诊断能力，降低了误判风险并改善了临床决策。
