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Updated: Sep 8, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Image processing approach provides robust feature extraction for classification with small sample sizes.

Alibek Zhakubayev¹, Thomas L Andersen^2,3, Annie Vesterby³

¹Department of Computer Science, Baylor University Waco, Texas, USA.

Proceedings of the 2023 7Th International Conference on Information System and Data Mining : Atlanta, GA, United States, May 10-12, 202. International Conference on Information System and Data Mining (7Th : 2023 : Atlanta, Ga.)

|August 26, 2025

Summary

This summary is machine-generated.

Chronic alcohol consumption impacts bone health. Novel image analysis techniques reveal subtle, detectable changes in bone architecture across species, aiding skeletal health research.

Keywords:

alcohol bone machine learning microcomputed tomography rhesus macaque

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Orthopedics and Imaging Analysis
Biomedical Engineering
Anthropology

Background:

Chronic alcohol consumption negatively affects skeletal health and bone architecture.
Medical imaging offers potential for machine learning-based analysis of alcohol's skeletal effects.
Small sample sizes in medical imaging studies hinder traditional machine learning approaches.

Purpose of the Study:

To develop a novel image feature extraction technique suitable for small datasets.
To analyze the impact of intrinsic (age, sex) and extrinsic (alcohol consumption) factors on bone architecture.
To assess the generalizability of the developed technique across species.

Main Methods:

Developed a novel image feature extraction technique for small image datasets.
Applied the technique to microcomputed tomography (micro-CT) images of human and non-human primate bone samples.
Trained machine learning models to classify features related to sex and alcohol consumption patterns.

Main Results:

Achieved high performance in distinguishing sex-based bone differences (72% in macaques, 65.5% in humans).
Successfully differentiated between drinking and non-drinking individuals in both macaques (68%) and humans (65%).
Demonstrated the technique's ability to capture generalizable biological features across species.

Conclusions:

The novel image processing approach can detect subtle effects of alcohol consumption on bone architecture.
The developed technique is effective even with small image datasets, showing cross-species applicability.
Findings highlight the potential of medical imaging for understanding alcohol's impact on skeletal health.