Detection of Explosives Using Image AnalysisKrithika Chandrasekar, Devang Parekh, Yichen Lu, Xiaodong Li

Shruthi Sanjeevi Reddy, Liqun YangPurdue University

School of Electrical and Computer Engineering

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 baggage(5)

Input: CT slices ofscanned baggage

Fig 4 Flow Diagram of Algorithm

COMPUTED TOMOGRAPHY (CT) The formation of a CT image is a distinct two phase process. •The scanning phase •The reconstruction phase

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 2. Formation of a CT image(2)

EXPLOSIVES • 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 materials

Fig 3. Absorption of explosives(3)

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)

Fig 6. CT Slice after applying Otsu’s Method (Step 1 of algorithm)

• 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

•Feature vector includes mean and variance for each object

• Significant changes in density distribution are detected

• Clustering is performed on the object

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

Overview

Methods and Approach

Review of Background Flowchart of Algorithm

References

Results (cont.)

Results

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/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

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 – 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