We analyse the effectiveness of edge detection algorithms for the purpose of
automatically extracting coastlines from satellite images. Four algorithms -
Canny, Sobel, Scharr and Prewitt are compared visually and using metrics. With
an average SSIM of 0.8, Canny detected edges that were closest to the reference
edges. However, the algorithm had difficulty distinguishing noisy edges, e.g.
due to development, from coastline edges. In addition, histogram equalization
and Gaussian blur were shown to improve the effectiveness of the edge detection
algorithms by up to 1.5 and 1.6 times respectively.

通过对边缘检测算法进行分析，我们研究了从卫星图像中自动提取海岸线的有效性。通过视觉和度量的比较，我们发现 Canny 算法的平均结构相似性指数（SSIM）为 0.8，检测到的边缘与参考边缘最为接近。然而，该算法在区分嘈杂边缘（如建筑物等）和海岸线边缘方面存在困难。此外，直方图均衡化和高斯模糊可分别将边缘检测算法的有效性提高 1.5 倍和 1.6 倍。

利用边缘检测算法的自动海岸线提取

Automated Coastline Extraction Using Edge Detection Algorithms

This study systematically investigates the impact of image enhancement
techniques on Convolutional Neural Network (CNN)-based Brain Tumor
Segmentation, focusing on Histogram Equalization (HE), Contrast Limited
Adaptive Histogram Equalization (CLAHE), and their hybrid variations. Employing
the U-Net architecture on a dataset of 3064 Brain MRI images, the research
delves into preprocessing steps, including resizing and enhancement, to
optimize segmentation accuracy. A detailed analysis of the CNN-based U-Net
architecture, training, and validation processes is provided. The comparative
analysis, utilizing metrics such as Accuracy, Loss, MSE, IoU, and DSC, reveals
that the hybrid approach CLAHE-HE consistently outperforms others. Results
highlight its superior accuracy (0.9982, 0.9939, 0.9936 for training, testing,
and validation, respectively) and robust segmentation overlap, with Jaccard
values of 0.9862, 0.9847, and 0.9864, and Dice values of 0.993, 0.9923, and
0.9932 for the same phases, emphasizing its potential in neuro-oncological
applications. The study concludes with a call for refinement in segmentation
methodologies to further enhance diagnostic precision and treatment planning in
neuro-oncology.

该研究系统地研究了图像增强技术对基于卷积神经网络（CNN）的脑肿瘤分割的影响，重点关注直方图均衡化（HE）、有限对比适应性直方图均衡化（CLAHE）及其混合变化。利用 3064 个脑 MRI 图像数据集，采用 U-Net 架构进行预处理步骤和优化分割精度的研究。通过比较分析和多项指标，研究结果表明 CLAHE-HE 混合方法始终表现出优越性，具有较高的准确度和鲁棒的分割重叠性，强调其在神经肿瘤应用中的潜力。研究结论呼吁进一步改进分割方法，以提高神经肿瘤的诊断精确性和治疗规划能力。