增强MRI脑肿瘤边缘检测：一种利用CLAHE的混合预处理方法
Enhancing MRI Brain Tumor Edge Detection: A Hybrid Preprocessing Approach Utilizing CLAHE
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中文总结 AI 辅助
针对MRI脑肿瘤边界检测难题，提出集成优化CLAHE的混合预处理流程，自动化阈值选择，在Kaggle基准库上提升了召回率、F1分数与SSIM，高效可用于临床诊断。
中文摘要 AI 辅助
在神经肿瘤学中，磁共振成像（MRI）中脑肿瘤的准确边界描绘是一项关键但艰巨的挑战，这是由于存在固有扫描仪噪声、复杂解剖结构和不均匀照明。传统边缘检测算法虽然计算量小且具有数学可解释性，但仅依赖全局预处理和手动参数调整时，常常无法捕捉水肿的弥漫性、局部边界。为克服这些局限性，我们提出了一种混合自动化边缘检测流程。该方法将优化配置的对比度受限自适应直方图均衡化（CLAHE）层集成到全面的形态学预处理框架中，随后采用确定性顺序参数扫描以完全自动化阈值选择。所提出的混合模型在Kaggle的公开基准数据库中展现出对关键解剖结构检测的增强效果。通过智能放大局部梯度而不被背景噪声淹没图像，我们的方法获得了更高的召回率（灵敏度），进而使整体F1分数提升，结构相似性指数（SSIM）也得到改善，同时保持了高效的执行效率。这确立了我们优化后的流程作为临床诊断中一种高度实用且接近实时运行的模型，为计算量大的深度学习方法提供了一种引人注目的替代方案。
英文摘要
Accurate boundary delineation of brain tumors in Magnetic Resonance Imaging (MRI) is a critical yet formidable challenge in neuro-oncology due to inherent scanner noise, complex anatomical structures, and uneven illumination. Traditional edge detection algorithms, while computationally lightweight and mathematically interpretable, frequently fail to capture the diffuse, localized boundaries of edema when relying solely on global preprocessing and manual parameter tuning. To overcome these limitations, we propose a hybrid automated edge detection pipeline. Our approach integrates an optimally configured Contrast-Limited Adaptive Histogram Equalization (CLAHE) layer into a comprehensive morphological preprocessing framework, followed by a deterministic sequential parameter sweep to fully automate threshold selection. The proposed hybrid model demonstrated enhancement in detecting critical anatomical structures in a publicly available benchmark database from Kaggle. By intelligently amplifying localized gradients without overwhelming the image with background noise, our method achieved higher Recall (Sensitivity). Consequently, the overall F1-Score elevated, and the Structural Similarity Index (SSIM) improved, all while maintaining a highly efficient execution. This establishes our optimized pipeline as a highly practical and near real-time operational model for clinical diagnostics, offering a compelling alternative to computationally heavy deep learning approaches.
发表机构
- Institute of Information Technology, University of Dhaka（达卡大学信息技术学院）
机构由 AI 辅助整理，请以论文原文为准。
