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Feature Selection Based on Iterative Canonical Correlation Analysis for Automatic Diagnosis of Parkinson’s Disease Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Wavelet Appearance Pyramids for Landmark Detection and Pathology Classification: Application to Lumbar Spinal Stenosis

Authors : Qiang Zhang, Abhir Bhalerao, Caron Parsons, Emma Helm, Charles Hutchinson

Published in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

Publisher: Springer International Publishing

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Abstract

Appearance representation and feature extraction of anatomy or anatomical features is a key step for segmentation and classification tasks. We focus on an advanced appearance model in which an object is decomposed into pyramidal complementary channels, and each channel is represented by a part-based model. We apply it to landmark detection and pathology classification on the problem of lumbar spinal stenosis. The performance is evaluated on 200 routine clinical data with varied pathologies. Experimental results show an improvement on both tasks in comparison with other appearance models. We achieve a robust landmark detection performance with average point to boundary distances lower than 2 pixels, and image-level anatomical classification with accuracies around \(85\,\%\).

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previous chapter Bayesian Image Quality Transfer
next chapter A Learning-Free Approach to Whole Spine Vertebra Localization in MRI
Footnotes
1
Supplementary videos of the paper can be found at https://​sites.​google.​com/​site/​waveletappearanc​epyramids.
 
2
Spectrum cropping as image downsampling is explained at https://​goo.​gl/​ApJJeL.
 
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Metadata
Title
Wavelet Appearance Pyramids for Landmark Detection and Pathology Classification: Application to Lumbar Spinal Stenosis
Authors
Qiang Zhang
Abhir Bhalerao
Caron Parsons
Emma Helm
Charles Hutchinson
Copyright Year
2016
Book
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

Print ISBN: 978-3-319-46722-1

Electronic ISBN: 978-3-319-46723-8

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46723-8_32

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