Detection of Explosives Using Image Analysis
Krithika Chandrasekar, Devang Parekh, Yichen Lu, Xiaodong Li
Shruthi Sanjeevi Reddy, Liqun Yang
Purdue University
School of Electrical and Computer Engineering
Overview
Review of Background
COMPUTED TOMOGRAPHY (CT)
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.
The formation of a CT image is a distinct two phase
process.
•The scanning phase
•The reconstruction phase
Flowchart of Algorithm
Results (cont.)
Input: CT slices of
scanned baggage
CT images have five specific image quality characteristics.
They are:
•Contrast Sensitivity (very high for CT)
•Blurring and visibility of Detail
•Visual Noise
•Artifacts
•Spatial (Tomographic slice or volume views)(2)
Fig 4 Flow Diagram of Algorithm
Results
References
Fig 1. 3D scanned image of baggage(5)
Fig 2. Formation of a CT image(2)
Methods and Approach
EXPLOSIVES
• The image slices are individually converted to
grayscale and Otsu thresholding is performed on them
• 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
• Connected component analysis is performed on the
slices to find regions of connected pixels
• A large percentage of nitrogen and oxygen can be a sign
of an explosive device
• The slices are assembled to obtain the 3D object
using region growing
• Currently, optical density and effective atomic numbers
are used to detect explosives(1)
• Individual objects are compared using feature vector
analysis to check if they have a uniform density
distribution
• Explosives have higher optical densities than nonexplosive materials
• The algorithm takes CT slices of screened baggage,
as its input
Fig 7. 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.
Fig 5. CT Slice of Pan Am data set (Input)
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/MedicalI
maging/Me dicalX-Rays/ucm115318.htm
2 CT image formation. Retrieved from
http://www.sprawls.org/resources/CTIMG/module.htm#1
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.
5Computed tomography for airport security. (2010)
http://www.analogic.com/about-us-overview.htm
Contact Infomation
•Feature vector includes mean and variance for each
object
Danger Detector
School of Electrical and Computer Engineering
Purdue University
West Lafayette, Indiana
• Significant changes in density distribution are detected
All questions or correspondence related to this document should be addressed to
• Clustering is performed on the object
• A match to known explosives is found by comparing
the attenuation co-efficient
Fig 6. CT Slice after applying Otsu’s Method (Step 1 of
algorithm)
Fig 3. Absorption of explosives(3)
Dr Charles A Bouman – bouman@purdue.edu
Krithika Chandrasekar – kchandra@purdue.edu
Devang Parekh – dparekh@purdue.edu
Yichen Lu – lu90@purdue.edu
Xiaodong Li – li5@purdue.edu
Shruthi Sanjeevi Reddy – s.shruthi89@purdue.edu
Liqun Yang – lyang@purdue.edu