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Deformable appearance pyramids for anatomy representation, landmark detection and pathology classification
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Zhang, Qiang, Bhalerao, Abhir and Hutchinson, Charles E. (2017) Deformable appearance pyramids for anatomy representation, landmark detection and pathology classification. International Journal of Computer Assisted Radiology and Surgery, 12 (8). pp. 1271-1280. doi:10.1007/s11548-017-1622-5 ISSN 1861-6410.
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Official URL: http://dx.doi.org/10.1007/s11548-017-1622-5
Abstract
Purpose
Representation of anatomy appearance is one of the key problems in medical image analysis. An appearance model represents the anatomies with parametric forms, which are then vectorised for prior learning, segmentation and classification tasks.
Methods
We propose a part-based parametric appearance model we refer to as a deformable appearance pyramid (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 variability within a population approximated by local translations of the multi-scale parts and linear appearance variations in the assembly of the parts. We introduce DAPs built on two types of image pyramids, namely Gaussian and wavelet pyramids, and present two approaches to model the prior and fit the model, one explicitly using a subspace Lucas–Kanade algorithm and the other implicitly using the supervised descent method (SDM).
Results
We validate the performance of the DAP instances with difference configurations on the problem of lumbar spinal stenosis for localising the landmarks and classifying the pathologies. We also compare them with classic methods such as active shape models, active appearance models and constrained local models. Experimental results show that the DAP built on wavelet pyramids and fitted with SDM gives the best results in both landmark localisation and classification.
Conclusion
A new appearance model is introduced with several configurations presented and evaluated. The DAPs can be readily applied for other clinical problems for the tasks of prior learning, landmark detection and pathology classification.
Item Type: | Journal Article | ||||||||
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Subjects: | R Medicine > RC Internal medicine | ||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
Library of Congress Subject Headings (LCSH): | Diagnostic imaging -- Mathematical models, Gaussian processes | ||||||||
Journal or Publication Title: | International Journal of Computer Assisted Radiology and Surgery | ||||||||
Publisher: | Springer | ||||||||
ISSN: | 1861-6410 | ||||||||
Official Date: | August 2017 | ||||||||
Dates: |
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Volume: | 12 | ||||||||
Number: | 8 | ||||||||
Page Range: | pp. 1271-1280 | ||||||||
DOI: | 10.1007/s11548-017-1622-5 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 9 June 2017 | ||||||||
Date of first compliant Open Access: | 9 June 2017 |
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