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Detection of explosives in baggage using tomographic reconstruction and image analysis

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  1. Detection of explosives in baggage using tomographic reconstruction and image analysis February 16, 2010 Purdue University Aziza Satkhozhina

  2. On average one busy international airport • 60 departures per hour • 200 passengers for each flight • 12,000 pieces of baggage to check each hour! Riveros, E. G. (2002, December 22). The digital radiographic and computed tomography imaging of two types of explosive devices. International Journal of Radiation Applications and Instrumentation, Retrieved February 15, 2010, from http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TJ0-4724JJ1-D&_user=29441&_coverDate=12%2F31%2F2002&_rdoc=1&_fmt=high&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1209341110&_rerunOrigin=google&_acct=C000003858&_version=1&_urlVersion=0&_userid=29441&md5=8b3ba91f68f01bd9cf71e317403ff406

  3. What is an explosive? An explosive material, also called an explosive, is a substance that contains a great amount of stored energy that can produce an explosion, a sudden expansion of the material after initiation, usually accompanied by the production of light, heat, and pressure . Explosive material. (2007). In Wikipedia. Retrieved February 2, 2010, from http://en.wikipedia.org/wiki/Explosive_material

  4. Classification of explosives • Propellants / low explosives containoxygenneeded for the combustion and produce gas which produces explosion ( black powder, smokeless powder) • Primary explosives/initiators arevery sensitive and explode when they are heated or subjected to shock , often used as detonators (lead azide, nitrogen sulfide etc). • High explosives areless sensitive than primary explosives, detonate under the influence of the shock of the explosion of a suitable primary explosive (nitroglycerin, acetylene, ammonium nitrate etc) Airport security is threatened by High Explosives (HE) Davis, T. (1972). Chemistry of Powder and Explosives. Angriff Press.

  5. Properties of explosives that help explosive detection • Elemental composition: many explosives contain large amount of oxygen and nitrogen • Density: most explosives have higher material density than other objects. Typical explosives that terrorists use fall in the range between 1.2 and 1.6 g/cm^3. http://en.wikipedia.org/wiki/Table_of_explosive_detonation_velocities

  6. CT images Spreading streaks in CT images – star artifacts • Mathematical distortion of the image when significant amount of impedance is mismatching • Appears when a high density object surrounded by low-density object Riveros, E. G. (2002, December 22). The digital radiographic and computed tomography imaging of two types of explosive devices. International Journal of Radiation Applications and Instrumentation, Retrieved February 15, 2010, from http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TJ0-4724JJ1-D&_user=29441&_coverDate=12%2F31%2F2002&_rdoc=1&_fmt=high&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1209341110&_rerunOrigin=google&_acct=C000003858&_version=1&_urlVersion=0&_userid=29441&md5=8b3ba91f68f01bd9cf71e317403ff406

  7. Three general approaches for image segmentation • Pixel- based approach uses information such as grey level, gradient magnitude, color independently from neighboring pixels and then histogram threshold is used (for ex. Otsu method) • Region-based approach takes the consideration of neighboring pixels and their relation to examined pixel (clustering, region growing) • Model-based approach considers image content and the noise • Thai, T. O. (1991, November 13). Segmentation of X-ray Images Using probabilistic relaxation labeling. First International Symposium on Explosive Detection Technology,

  8. Image segmentation methods • Clustering - separating the image into clusters so that each cluster consisted of objects with similar property (f.e. clustering by color). Popular methods are k-means, EM etc VDBSCAN (Varied-density Based Spatial Clustering of Applications with Noise) Advantages: • does not require the number of clusters priori unlike k-means • can find arbitrarily shaped cluster • has a notion of noise • detects clusters in data with varying densities Disadvantage: • Complexity Wikipedia. (2010). In OPTICS algorithm. Retrieved February 15, 2010, from http://en.wikipedia.org/wiki/OPTICS

  9. A region-growing • First seeds (centers of regions) are chosen and then neighbor points are joining based on some criteria. Advantage: • Separate regions that have the same properties • Clear edges • Simple • Several criteria at a time • Performs well with respect to the noise Disadvantage: • Computation is consuming • Noise or variation in intensity can result in holes or over segmentation • Can’t distinguish the shading of real objects • Note: noise can be filtered out so that the noise disadvantage can be eliminated • Wikipedia. (2010). In Region growing. Retrieved February 15, 2010, from http://en.wikipedia.org/wiki/Region_growing