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Automatic Segmentation of Unknown Objects, with Application to Baggage Security

  • Leo Grady
  • Vivek Singh
  • Timo Kohlberger
  • Christopher Alvino
  • Claus Bahlmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7573)

Abstract

Computed tomography (CT) is used widely to image patients for medical diagnosis and to scan baggage for threatening materials. Automated reading of these images can be used to reduce the costs of a human operator, extract quantitative information from the images or support the judgements of a human operator. Object quantification requires an image segmentation to make measurements about object size, material composition and morphology. Medical applications mostly require the segmentation of prespecified objects, such as specific organs or lesions, which allows the use of customized algorithms that take advantage of training data to provide orientation and anatomical context of the segmentation targets. In contrast, baggage screening requires the segmentation algorithm to provide segmentation of an unspecified number of objects with enormous variability in size, shape, appearance and spatial context. Furthermore, security systems demand 3D segmentation algorithms that can quickly and reliably detect threats. To address this problem, we present a segmentation algorithm for 3D CT images that makes no assumptions on the number of objects in the image or on the composition of these objects. The algorithm features a new Automatic QUality Measure (AQUA) model that measures the segmentation confidence for any single object (from any segmentation method) and uses this confidence measure to both control splitting and to optimize the segmentation parameters at runtime for each dataset. The algorithm is tested on 27 bags that were packed with a large variety of different objects.

Keywords

Ground Truth Image Segmentation Gaussian Mixture Model Segmentation Algorithm Object Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Leo Grady
    • 1
  • Vivek Singh
    • 2
  • Timo Kohlberger
    • 2
  • Christopher Alvino
    • 3
  • Claus Bahlmann
    • 2
  1. 1.HeartFlow, Inc.Redwood CityUSA
  2. 2.Corporate Research and TechnologyImaging and Computer Vision, Siemens CorporationPrincetonUSA
  3. 3.American Science and EngineeringBillericaUSA

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