Deformable appearance pyramids for anatomy representation, landmark detection and pathology classification

Deformable appearance pyramids for anatomy representation, landmark detection and pathology... Int J CARS (2017) 12:1271–1280 DOI 10.1007/s11548-017-1622-5 ORIGINAL ARTICLE Deformable appearance pyramids for anatomy representation, landmark detection and pathology classification 1 1 2 Qiang Zhang · Abhir Bhalerao · Charles Hutchinson Received: 28 January 2017 / Accepted: 23 May 2017 / Published online: 3 June 2017 © The Author(s) 2017. This article is an open access publication Abstract Conclusion A new appearance model is introduced with Purpose Representation of anatomy appearance is one of several configurations presented and evaluated. The DAPs the key problems in medical image analysis. An appearance can be readily applied for other clinical problems for the model represents the anatomies with parametric forms, which tasks of prior learning, landmark detection and pathology are then vectorised for prior learning, segmentation and clas- classification. sification tasks. Methods We propose a part-based parametric appearance Keywords Deformable part models · Deformable appear- model we refer to as a deformable appearance pyramid ance pyramids · Landmark detection · Classification (DAP). The parts are delineated by multi-scale local feature pyramids extracted from an image pyramid. Each anatomy is represented by an appearance pyramid, with the variabil- Introduction ity within a population approximated by local translations of the multi-scale parts and linear http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Computer Assisted Radiology and Surgery Springer Journals

Deformable appearance pyramids for anatomy representation, landmark detection and pathology classification

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Publisher
Springer International Publishing
Copyright
Copyright © 2017 by The Author(s)
Subject
Medicine & Public Health; Imaging / Radiology; Surgery; Health Informatics; Computer Imaging, Vision, Pattern Recognition and Graphics; Computer Science, general
ISSN
1861-6410
eISSN
1861-6429
D.O.I.
10.1007/s11548-017-1622-5
Publisher site
See Article on Publisher Site

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