融合模糊与自适应直方图增强，提升眼底血管分割效果
A Novel Retinal Image Contrast Enhancement -- Fuzzy-Based Method
- 用模糊对比度增强与自适应直方图均衡融合提升图像质量
- 在DRIVE数据集上，组合方法使血管增强效果优于单一方法
- 适合眼科医学图像处理研究者参考
眼底图像中的血管结构对眼科诊断至关重要，其准确性直接取决于图像质量。对比度增强是分割算法的关键步骤，尤其在医学影像中，需同时增强血管亮度并避免微小毛细血管被忽略。本文提出一种新模型，将模糊对比度增强（FCE）与对比度受限自适应直方图均衡化（CLAHE）进行线性融合，用于眼底图像增强以支持血管结构分割。该方法在Digital Retinal Images for Vessel Extraction（DRIVE）数据集上测试，并与灰度化、直方图均衡化（HE）、FCE、CLAHE等方法对比。结果表明，FCE与CLAHE的结合显著提升增强效果，二者单独使用时均达到88%的优异表现，验证了模糊逻辑预处理的有效性。
原文摘要 · Abstract (English)
The vascular structure in retinal images plays a crucial role in ophthalmic diagnostics, and its accuracies are directly influenced by the quality of the retinal image. Contrast enhancement is one of the crucial steps in any segmentation algorithm - the more so since the retinal images are related to medical diagnosis. Contrast enhancement is a vital step that not only intensifies the darkness of the blood vessels but also prevents minor capillaries from being disregarded during the process. This paper proposes a novel model that utilizes the linear blending of Fuzzy Contrast Enhancement (FCE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the retinal image for retinal vascular structure segmentation. The scheme is tested using the Digital Retinal Images for Vessel Extraction (DRIVE) dataset. The assertion was then evaluated through performance comparison among other methodologies which are Gray-scaling, Histogram Equalization (HE), FCE, and CLAHE. It was evident in this paper that the combination of FCE and CLAHE methods showed major improvement. Both FCE and CLAHE methods dominating with 88% as better enhancement methods proved that preprocessing through fuzzy logic is effective.
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