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Published on: April 5, 2020
Detection and segmentation of concealed objects in terahertz images
Xilin Shen1, Charles R Dietlein, Erich Grossman
1Department of Radiology, Yale University, New Haven, CT 06519, USA.
Summary
This study introduces a novel method for automatically detecting concealed objects using terahertz imaging. The unsupervised approach effectively segments hidden items in low-contrast images, outperforming existing methods.
Area of Science:
- Applied Physics
- Image Processing
- Biomedical Imaging
Background:
- Passive terahertz imaging offers potential for detecting concealed objects by analyzing radiometric temperatures.
- Terahertz images suffer from low contrast and signal-to-noise ratio, hindering standard image segmentation algorithms.
- Existing methods struggle to accurately detect and segment objects hidden under clothing.
Purpose of the Study:
- To develop an automated, unsupervised method for detecting and segmenting concealed objects in terahertz images.
- To address the challenges posed by poor image quality in passive terahertz imaging.
- To improve upon the performance of current state-of-the-art segmentation techniques for hidden object detection.
Main Methods:
- Implemented a two-stage approach: noise reduction using anisotropic diffusion and object boundary detection.
- Utilized a mixture of Gaussian densities to model image temperature distributions.
- Employed evolving curves along image isocontours for object identification.
Main Results:
- The proposed method successfully detected and segmented concealed objects where standard algorithms failed.
- Outperformed two state-of-the-art unsupervised segmentation methods.
- Demonstrated higher accuracy than a supervised method, without requiring prior object identification.
Conclusions:
- The developed unsupervised terahertz image segmentation method is effective for detecting concealed objects.
- The approach overcomes limitations of existing algorithms in low-quality imaging scenarios.
- Potential for real-time application on dedicated hardware for security and screening.
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