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Subcutaneous tissue structural feature identification using unsupervised machine learning
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DOI：10.1016/j.compbiomed.2026.111553.png)
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Abstract
En 中文
• 2D proximal intensity mapping in polar coordinates to capture structural details. • K-means clustering for segmentation and feature identification. • Rotational invariance handling via neighborhood analysis in polar coordinates. • Data reduction techniques to improve feature vector separability and clustering accuracy. • Demonstrated application in stained histology images, enabling identification of subcutaneous tissue structural features.
Keywords:
2D proximal intensity mapping
K-means clustering
polar coordinates
rotational invariance
histology images
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