Detection of explosives using image analysis
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Detection of explosives using image analysis Dr Charles A Bouman, Eri Haneda, Aarthi Balachander, Krithika Chandrasekar, Govind Manian, Charvaka Mattaparthy, Aziza Satkhozhina School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana. Overview.

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Detection of explosives using image analysis

Detection of explosives using image analysis

Dr Charles A Bouman, Eri Haneda, Aarthi Balachander, Krithika Chandrasekar, Govind Manian, Charvaka Mattaparthy, Aziza Satkhozhina

School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana

Overview

Review of Background

Flowchart of Algorithm

Results (cont.)

Input: CT slices of scanned baggage

  • Recent security threats in airports have resulted in the need for sophisticated explosive detection techniques. This project aims to write an algorithm based on image analysis techniques to detect explosives in baggage and eliminate false alarms in screening equipment at airports. The algorithm will focus on analyzing differences in density distribution across the 3D volume of objects assembled using region growing.

  • Fig 1. 3D scanned image of baggage6

  • The algorithm takes CT slices of screened baggage, as its input.

  • The image slices are individually converted to grayscale and Otsu thresholding is performed on them.

  • Connected component analysis is performed on the slices to find regions of connected pixels.

  • The slices are assembled to obtain the 3D object using region growing.

  • Individual objects are compared using feature vector analysis to check if they have a uniform density distribution.

  • Significant changes in density distribution are detected.

  • K-means clustering is performed on the object.

  • A match to known explosives is found by comparing the attenuation co-efficient.

  • COMPUTED TOMOGRAPHY

  • Airports use computed tomography (CT) to detect thin objects such as sheet explosives.

  • CT scanners are commonly used for non-invasive medical imaging and have 3 steps

    • 1. The object is passed through a radiation source and detector and 2D images of the object are taken

    • 2.Assembles a 3D reconstruction from 2D images about a single axis of rotation.2

    • 3.The images are reconstructed using a computer-based process.6

  • The images are analyzed using Fourier Slice Theorem

  • EXPLOSIVES

  • Thresholding, binarization, and image segmentation are involved in explosive detection.

  • An explosive is a substance that contains a great amount of stored energy that can produce an explosion, or sudden expansion of the material

  • Airport security is threatened by explosives called high explosives (HE),1

  • Explosives are classified by sensitivity, velocity, composition and other properties.4

  • Two key properties are used to detect explosives

  • 1.Geometry: the presence of metallic detonator and associated wires can be detected using image shape analysis.4

  • 2.Elemental composition and material density: the explosive consists of oxidant and reductant.

  • A large percentage of nitrogen and oxygen can be a sign of an explosive device

  • Currently, optical density and effective atomic numbers are used to detect explosives. 1

  • Explosives have higher optical densities than non-explosive material s

Step 2: Connected Component analysis to group together pixel regions of interest

(OUTPUT: connected regions of pixels for each CT slice)

Step 1: Otsu Thresholding

(OUTPUT: image with two distinct classes of pixels)

Step 5: K means clustering to determine regions of change in linear attenuation co-efficient

Step 3: Region Growing across multiple slices to obtain the 3D object

Step 4: Feature vector determination for K-means clustering

Step 6: Comparison with attenuation co-efficient of known explosives

Output: Location of explosive in baggage

Fig 2 Flow Diagram of Algorithm

Results

Fig 5. Pixel intensity histogram of slice 20 (graphical verification of Otsu threshold). The threshold value found to minimize intra-class variance for this slice is 0.1255.

References

Methods and Approach

1Committee on the Review of Existing and Potential Standoff Explosives Detection Techniques, National Research Council, (2004). Existing and Potential Standoff Explosives Detection Techniques. National Academies Press.

EmittingProducts/RadiationEmittingProductsandProcedures/MedicalImaging/MedicalX-Rays/ucm115318.htm

2FDA. (2009, June 18). What is Computed tomography?. Retrieved from http://www.fda.gov/Radiation- Herman, G.T. (2009).

3Hu, Y., Huang, P., Guo, L., Wang, X., & Zhang, C. (2006). Terahertz spectroscopic investigations of explosives. Physics Letters A, 359, 728-32.

4Singh, S., & Singh, M. (2003). Explosives detection systems (EDS) for aviation security. Signal Processing, 83, 31-55.

5UTCT. (2009). About High-resolution x-ray ct. Retrieved from http://www.ctlab.geo.utexas.edu/overview/index.php

6Computed tomography for airport security. (2010.).

http://www.analogic.com/about-us-overview.htm

Fig 3. CT Slice 20 of Pan Am data set (Input)

Danger Detector

School of Electrical and Computer Engineering

Purdue University

West Lafayette, Indiana

All questions or correspondence related to this document should be addressed to

Dr Charles A Bouman – [email protected]

Ms Eri Haneda – [email protected]

Aarthi Balachander – [email protected]

Krithika Chandrasekar – [email protected]

Govind Manian – [email protected]

Charvaka Mattaparthy – [email protected]

Aziza Satkhozhina - [email protected]

Contact information

Fig 1. Absorption of explosives3

Fig 4. CT Slice after applying Otsu’s Method (Step 1 of algorithm)


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