发表机构
Department of Mathematics, Rhodes University, PO Box 94, Makhanda, 6140, South Africa; Department of Statistics, Rhodes University, PO Box 94, Makhanda, 6140, South Africa; National Institute for Theoretical and Computational Sciences (NITheCS), Stellenbosch 7600, South Africa（数学系，罗德斯大学; 统计系，罗德斯大学; 理论与计算科学国家研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对医学图像分析中利用非局部自相似性的聚类技术进行性能基准测试与优化，评估了五种聚类算法，经随机搜索优化后用多种指标评估，得出不同算法在不同模态下的表现及适用性结论。
AI 中文摘要
医学成像产生的高分辨率图像带来存储、传输和计算挑战。低秩矩阵近似技术虽能有效压缩，但全局方法常无法保留诊断关键的局部细节。本文聚焦利用非局部自相似性的聚类技术识别医学图像中结构相似区域，用于自适应图像压缩等后处理任务。评估了五种聚类技术，通过随机搜索优化并使用多种指标评估聚类质量。结果表明标准k均值和二分k均值聚类凝聚力和分离性强，但簇内变异性大；凝聚聚类在MRI和超声图像的簇内同质性方面表现优于其他技术；对于胸部X光，小批量k均值在聚类质量和簇内紧凑性之间实现了最佳平衡；BIRCH在所有模态下表现均不佳。
英文摘要
Medical imaging generates high-resolution images posing significant storage, transmission, and computational challenges. While low-rank matrix approximation (LoRMA) techniques offer efficient compression by exploiting structural redundancy, global approaches often fail to preserve local details critical for diagnosis. This paper focuses on clustering techniques that exploit non-local self-similarity to identify structurally similar regions in medical images. These clusters can be used for post-processing tasks such as adaptive image compression. We evaluate five clustering techniques: k-means, mini-batch k-means, agglomerative hierarchical clustering, balanced iterative reducing and clustering using hierarchies (BIRCH), and bisecting k-means across MRI, ultrasound, and chest X-ray modalities. All clustering techniques were optimised using random search, and cluster quality was assessed using the Silhouette score, the Davies-Bouldin (DB) index, and the Calinski-Harabasz (CH) index. Results demonstrate that standard k-means and bisecting k-means generally achieve strong cluster cohesion and separation across modalities. However, they tend to form a small number of clusters with high intra-cluster variability, limiting their effectiveness for post-processing tasks such as adaptive compression. Agglomerative clustering outperformed other techniques for MRI and ultrasound in terms of intra-cluster homogeneity, making it more suitable for preserving fine diagnostic details. For chest X-rays, mini-batch k-means achieved the best balance between clustering quality and intra-cluster compactness. BIRCH consistently underperformed across all modalities.
