Advancements in dermoscopy techniques have elucidated identifiable characteristics of melanoma which revolve around the asymmetrical constitution of melanocytic lesions consequent of unfettered proliferative growth as a malignant lesion. This study explores the applications of hierarchical density-based spatial clustering of applications with noise (HDBSCAN) in terms of the direct diagnostic implications of applying agglomerative clustering in the spectroscopic analysis of malignant melanocytic lesions and benign dermatologic spots. 100 images of benign (n = 50) and malignant moles (n = 50) were sampled from the International Skin Imaging Collaboration Archive and processed through two separate Python algorithms. The first of which deconvolutes the three-digit tupled integer identifiers of pixel color in image composition into three separate matrices corresponding to the red, green and blue color channel. Statistical characterization of integer variance was utilized to determine the optimal channel for comparative analysis between malignant and benign image groups. The second applies HDBSCAN to the matrices, identifying agglomerative clustering in the dataset. The results indicate the potential diagnostic applications of HDBSCAN analysis in fast-processing dermoscopy, as optimization of clustering parameters according to a binary search strategy produced an accuracy of 85% in the classification of malignant and benign melanocytic lesions.

中文翻译：

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将基于分层密度的空间聚类应用到光谱分析和黑素细胞病变检测的噪声应用的新策略。

皮肤镜技术的进步阐明了黑色素瘤的可识别特征，这些特征围绕着黑素细胞病变的不对称构成，这是由于恶​​性病变不受限制的增殖性生长而导致的。本研究探讨了基于分层密度的噪声应用空间聚类 (HDBSCAN) 的应用，探讨了在恶性黑素细胞病变和良性皮肤斑点的光谱分析中应用凝聚聚类的直接诊断意义。从国际皮肤成像协作档案中采样了 100 张良性痣 (n = 50) 和恶性痣 (n = 50) 的图像，并通过两个单独的 Python 算法进行处理。第一个将图像合成中像素颜色的三位数元组整数标识符解卷积为与红色、绿色和蓝色通道相对应的三个单独的矩阵。利用整数方差的统计特征来确定恶性和良性图像组之间比较分析的最佳通道。第二个将 HDBSCAN 应用于矩阵，识别数据集中的凝聚聚类。结果表明 HDBSCAN 分析在快速处理皮肤镜检查中的潜在诊断应用，因为根据二分搜索策略优化聚类参数，在恶性和良性黑素细胞病变分类中的准确度达到 85%。