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Computed Tomography Image Origin Identification Based on Original Sensor Pattern Noise and 3-D Image Reconstruction

Yuping Duan, Dalel Bouslimi, Guanyu Yang

IEEE Journal of Biomedical and Health Informatics

|June 14, 2016

Summary

This summary is machine-generated.

This study introduces novel noise features to identify the specific computed tomography (CT) scanner that created an image. This method achieves over 94% accuracy, outperforming existing techniques for CT scanner identification.

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Area of Science:

Medical Imaging
Computer Vision
Digital Forensics

Background:

Accurate identification of Computed Tomography (CT) scanners is crucial for medical imaging analysis and forensic investigations.
Existing methods for identifying imaging devices often lack specificity or are not optimized for the unique noise characteristics of CT scanners.

Purpose of the Study:

To develop a robust method for the "blind" identification of CT scanners based on image-derived noise features.
To differentiate between CT scanners from various manufacturers using intrinsic noise patterns and reconstruction algorithm modifications.

Main Methods:

Proposed two approaches: one using Original Sensor Pattern Noise (OSPN) and another analyzing noise modifications by 3D image reconstruction algorithms.
Utilized a Support Vector Machine (SVM) classifier trained on extracted noise features to discriminate between CT acquisition systems.
Evaluated the system on CT images from 15 different scanner models across 4 manufacturers.

Main Results:

Achieved a detection rate of at least 94% in identifying the origin of CT images.
Demonstrated superior performance compared to the Sensor Pattern Noise (SPN) based strategy used for general camera devices.
Successfully discriminated between CT scanners based on manufacturer-specific noise footprints.

Conclusions:

The proposed noise feature-based method provides an effective and accurate approach for CT scanner identification.
This technique offers a valuable tool for digital forensics and quality control in medical imaging.
The method's ability to overcome proprietary reconstruction algorithms highlights its practical applicability.