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Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

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Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis

Rini Widyaningrum¹, Enny Itje Sela², Reza Pulungan³

¹Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia.

International Journal of Dentistry

|March 10, 2023

Summary

This study introduces an automated method using dental X-rays and machine learning to detect osteoporosis. The best approach combined K-means segmentation with a multilayer perceptron classifier, achieving high accuracy in identifying bone density loss.

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

Medical Imaging
Artificial Intelligence
Bone Health

Background:

Osteoporosis causes bone loss, reduced bone mineral density (BMD), and increased fracture risk.
Dental periapical radiographs can reveal changes in trabecular bone structure associated with osteoporosis.

Purpose of the Study:

To develop and evaluate an automatic trabecular bone segmentation method for osteoporosis detection using periapical radiographs.
To compare K-means and Fuzzy C-means segmentation algorithms combined with machine learning classifiers.

Main Methods:

The study utilized 120 regions of interest (ROI) from periapical radiographs, split into training and testing datasets.
An automated method involving grayscale conversion, color histogram segmentation, and pixel distribution extraction was employed.
K-means and Fuzzy C-means were compared for segmentation, with results fed into decision tree, naive Bayes, and multilayer perceptron classifiers.

Main Results:

The combination of K-means segmentation and a multilayer perceptron classifier demonstrated the highest diagnostic performance.
This optimal method achieved an accuracy of 90.48%, specificity of 90.90%, and sensitivity of 90.00% on the testing dataset.

Conclusions:

The proposed automated method using K-means segmentation and multilayer perceptron shows significant potential for osteoporosis detection.
This technique offers a valuable contribution to medical and dental image analysis for early osteoporosis diagnosis.