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X-ray Imaging

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with X-rays, and by 1900, X-ray was widely...

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Updated: Jul 13, 2026

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

Multiple semantic X-ray medical image retrieval using efficient feature vector extracted by FPN.

Lijia Zhi^1,2, Shaoyong Duan¹, Shaomin Zhang^1,2

¹School of Computer Science and Engineering, North Minzu University, Yinchuan, China.

Journal of X-Ray Science and Technology

|July 20, 2024

Summary

This study introduces a deep convolutional neural network (CNN) for enhanced content-based medical image retrieval (CBMIR). The model improves the accuracy of retrieving multiple semantic X-ray images.

Keywords:

CBMIR IRMA X-Ray image multiple semantic retrieval

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Area of Science:

Medical Imaging
Computer-Aided Diagnostics
Artificial Intelligence

Background:

Content-based medical image retrieval (CBMIR) is crucial for computer-aided diagnostics (CAD).
Extracting complex semantic information from medical images is challenging for accurate retrieval.
Expressive feature vectors are essential for effective image search.

Purpose of the Study:

To propose a deep convolutional neural network (CNN) model for extracting concise feature vectors.
To enhance the accuracy of multiple semantic X-ray medical image retrieval.

Main Methods:

A feature pyramid-based CNN model with a ResNet50V2 backbone was developed.
The model extracts multi-level semantic information from X-ray images.
The IRMA dataset, a public multiple semantic annotated X-ray medical image dataset, was used for training and testing.

Main Results:

The proposed method achieved an IRMA error of 32.2.
This score represents the best performance reported in existing literature for the IRMA dataset.

Conclusions:

The CNN model effectively extracts multi-level semantic information from X-ray images.
Concise feature vectors improve retrieval accuracy for multi-semantic and unevenly distributed X-ray images.