Automatic segmentation of unknown objects, with application to baggage security

  • Authors:

  • Leo Grady;Vivek Singh;Timo Kohlberger;Christopher Alvino;Claus Bahlmann

  • Affiliations:

  • HeartFlow, Inc., Redwood City, CA;Corporate Research and Technology, Imaging and Computer Vision, Siemens Corporation, Princeton, NJ;Corporate Research and Technology, Imaging and Computer Vision, Siemens Corporation, Princeton, NJ;American Science and Engineering, Billerica, MA;Corporate Research and Technology, Imaging and Computer Vision, Siemens Corporation, Princeton, NJ

  • Venue:
  • ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
  • Year:
  • 2012

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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.