Subcutaneous tissue structural feature identification using unsupervised machine learning

doi:10.1016/j.compbiomed.2026.111553

Subcutaneous tissue structural feature identification using unsupervised machine learning

2026-02-24

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AI

Adrián Buganza Tepole

P

Pavlos P. Vlachos *

DOI：10.1016/j.compbiomed.2026.111553

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Abstract

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• 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

Subcutaneous tissue structural feature identification using unsupervised machine learning

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