Saliency-based rotation invariant descriptor for wrist detection in whole body ctimages

Mingchen Gao; Yiqiang Zhan; Gerardo Hermosillo; Yoshihisa Shinagawa; Dimitris Metaxas; Xiang Sean Zhou

doi:https://doi.org/10.1109/isbi.2014.6867823

Saliency-based rotation invariant descriptor for wrist detection in whole body ctimages

Mingchen Gao, Yiqiang Zhan, Gerardo Hermosillo, Yoshihisa Shinagawa, Dimitris Metaxas, Xiang Sean Zhou

Rutgers, The State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

In this paper, we propose a saliency-based rotation invariant descriptor and apply it to detect wrists in CT images. The descriptor is motivated by the observation that salient landmarks around wrists usually form a characteristic spatial configuration (Fig. 1). In our framework, a set of interest points are firstly computed via scale-space analysis. For each interest point, we compute a pyramid of scale-distance 2D histograms constructed with neighboring interest points. The descriptor represents the spatial configuration among neighboring interest points in a rotation-invariant fashion. A cascade set of random forests are trained to distinguish wrist from other anatomies using this descriptor. Our algorithm shows robust and accurate performance on 41 whole body CT scans with diverse context, orientations and articulation configurations.

Original language	American English
Title of host publication	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	117-120
Number of pages	4
ISBN (Electronic)	9781467319591
DOIs	https://doi.org/10.1109/isbi.2014.6867823
State	Published - Jul 29 2014
Event	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China Duration: Apr 29 2014 → May 2 2014

Publication series

Name	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Other

Other	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Country/Territory	China
City	Beijing
Period	4/29/14 → 5/2/14

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Keywords

Anatomical structure detection
Interest point descriptor
Rotation invariant
Wrist detection

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https://doi.org/10.1109/isbi.2014.6867823

Cite this

Gao, M., Zhan, Y., Hermosillo, G., Shinagawa, Y., Metaxas, D., & Zhou, X. S. (2014). Saliency-based rotation invariant descriptor for wrist detection in whole body ctimages. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 117-120). [6867823] (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/isbi.2014.6867823

Gao, Mingchen ; Zhan, Yiqiang ; Hermosillo, Gerardo et al. / Saliency-based rotation invariant descriptor for wrist detection in whole body ctimages. 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 117-120 (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014).

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title = "Saliency-based rotation invariant descriptor for wrist detection in whole body ctimages",

abstract = "In this paper, we propose a saliency-based rotation invariant descriptor and apply it to detect wrists in CT images. The descriptor is motivated by the observation that salient landmarks around wrists usually form a characteristic spatial configuration (Fig. 1). In our framework, a set of interest points are firstly computed via scale-space analysis. For each interest point, we compute a pyramid of scale-distance 2D histograms constructed with neighboring interest points. The descriptor represents the spatial configuration among neighboring interest points in a rotation-invariant fashion. A cascade set of random forests are trained to distinguish wrist from other anatomies using this descriptor. Our algorithm shows robust and accurate performance on 41 whole body CT scans with diverse context, orientations and articulation configurations.",

keywords = "Anatomical structure detection, Interest point descriptor, Rotation invariant, Wrist detection",

author = "Mingchen Gao and Yiqiang Zhan and Gerardo Hermosillo and Yoshihisa Shinagawa and Dimitris Metaxas and Zhou, {Xiang Sean}",

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Gao, M, Zhan, Y, Hermosillo, G, Shinagawa, Y, Metaxas, D & Zhou, XS 2014, Saliency-based rotation invariant descriptor for wrist detection in whole body ctimages. in 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014., 6867823, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Institute of Electrical and Electronics Engineers Inc., pp. 117-120, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, China, 4/29/14. https://doi.org/10.1109/isbi.2014.6867823

Saliency-based rotation invariant descriptor for wrist detection in whole body ctimages. / Gao, Mingchen; Zhan, Yiqiang; Hermosillo, Gerardo et al.

2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 117-120 6867823 (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zhan, Yiqiang

AU - Hermosillo, Gerardo

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AU - Metaxas, Dimitris

AU - Zhou, Xiang Sean

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N2 - In this paper, we propose a saliency-based rotation invariant descriptor and apply it to detect wrists in CT images. The descriptor is motivated by the observation that salient landmarks around wrists usually form a characteristic spatial configuration (Fig. 1). In our framework, a set of interest points are firstly computed via scale-space analysis. For each interest point, we compute a pyramid of scale-distance 2D histograms constructed with neighboring interest points. The descriptor represents the spatial configuration among neighboring interest points in a rotation-invariant fashion. A cascade set of random forests are trained to distinguish wrist from other anatomies using this descriptor. Our algorithm shows robust and accurate performance on 41 whole body CT scans with diverse context, orientations and articulation configurations.

AB - In this paper, we propose a saliency-based rotation invariant descriptor and apply it to detect wrists in CT images. The descriptor is motivated by the observation that salient landmarks around wrists usually form a characteristic spatial configuration (Fig. 1). In our framework, a set of interest points are firstly computed via scale-space analysis. For each interest point, we compute a pyramid of scale-distance 2D histograms constructed with neighboring interest points. The descriptor represents the spatial configuration among neighboring interest points in a rotation-invariant fashion. A cascade set of random forests are trained to distinguish wrist from other anatomies using this descriptor. Our algorithm shows robust and accurate performance on 41 whole body CT scans with diverse context, orientations and articulation configurations.

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Gao M, Zhan Y, Hermosillo G, Shinagawa Y, Metaxas D, Zhou XS. Saliency-based rotation invariant descriptor for wrist detection in whole body ctimages. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 117-120. 6867823. (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014). doi: https://doi.org/10.1109/isbi.2014.6867823

Saliency-based rotation invariant descriptor for wrist detection in whole body ctimages

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Keywords

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