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<p>Clustering high-dimensional biomedical data without prior knowledge of the number of clusters remains a major challenge in medical image and signal analysis. We present <b>SONSC</b> (Separation-Optimized Number of Smart Clusters), an adaptive and interpretable clustering framework driven by the <i>Improved Separation Index (ISI)</i>—a novel internal validity metric that jointly evaluates intra-cluster compactness and inter-cluster separability. SONSC iteratively maximizes ISI across candidate cluster configurations to automatically infer the optimal number of clusters, without supervision or parameter tuning. Extensive experiments on benchmark datasets (MNIST, CIFAR-10) and real-world clinical modalities (chest X-ray, ECG, RNA-seq) demonstrate that SONSC consistently outperforms classical methods such as K-Means, DBSCAN, and spectral clustering in ISI, Silhouette score, and normalized mutual information (NMI). Beyond numerical performance, SONSC identifies clinically coherent structures aligned with expert-labeled categories, supporting its integration into diagnostic and patient stratification pipelines. By unifying algorithmic robustness with medical interpretability, SONSC provides a scalable and trustworthy solution for unsupervised biomedical data analysis.</p>

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Adaptive clustering for medical image analysis using the improved separation index

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月1日

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  • Mojtaba Jahanian,
  • Abbas Karimi,
  • Nafiseh Osati Eraghi,
  • Faraneh Zarafshan

摘要

Clustering high-dimensional biomedical data without prior knowledge of the number of clusters remains a major challenge in medical image and signal analysis. We present SONSC (Separation-Optimized Number of Smart Clusters), an adaptive and interpretable clustering framework driven by the Improved Separation Index (ISI)—a novel internal validity metric that jointly evaluates intra-cluster compactness and inter-cluster separability. SONSC iteratively maximizes ISI across candidate cluster configurations to automatically infer the optimal number of clusters, without supervision or parameter tuning. Extensive experiments on benchmark datasets (MNIST, CIFAR-10) and real-world clinical modalities (chest X-ray, ECG, RNA-seq) demonstrate that SONSC consistently outperforms classical methods such as K-Means, DBSCAN, and spectral clustering in ISI, Silhouette score, and normalized mutual information (NMI). Beyond numerical performance, SONSC identifies clinically coherent structures aligned with expert-labeled categories, supporting its integration into diagnostic and patient stratification pipelines. By unifying algorithmic robustness with medical interpretability, SONSC provides a scalable and trustworthy solution for unsupervised biomedical data analysis.