An automatic detection algorithm of MR images for knee pain problem

Yi-Nung Chung, Cheng Nan Chou, Haw Chang Lan, Wen Hsin Ho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Anterior knee pain (AKP) is a common pathological condition. The problem causing knee pain is the abnormal patellar tracking mechanism. Kinematic approaches using MR images have been regarded of more accuracy in knee pain detection than stationary approaches. This paper proposes an automatic diagnosis based kinematic patellar tracking for AKP detection. The kinematic patellar tracking uses a hybrid approach for extracting knee organs, where an edge-constrained wavelet enhancement followed by moment preserving segmentation is employed for conquering the soft tissue adhesion for extracting the femur and tibia from axial MR images, and a sliding window based moment preserving for resolving the segmentation difficulty associated with intensity non-uniformity in saggital MR images. The experiment results demonstrate the prominent of the calculated inclination angles in detecting AKP.

Original languageEnglish
Title of host publicationTENCON 2007 - 2007 IEEE Region 10 Conference
DOIs
Publication statusPublished - 2007 Dec 1
EventIEEE Region 10 Conference, TENCON 2007 - Taipei, Taiwan
Duration: 2007 Oct 302007 Nov 2

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON

Other

OtherIEEE Region 10 Conference, TENCON 2007
CountryTaiwan
CityTaipei
Period07-10-3007-11-02

Fingerprint

Kinematics
Adhesion
Tissue
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Chung, Y-N., Chou, C. N., Lan, H. C., & Ho, W. H. (2007). An automatic detection algorithm of MR images for knee pain problem. In TENCON 2007 - 2007 IEEE Region 10 Conference [4428840] (IEEE Region 10 Annual International Conference, Proceedings/TENCON). https://doi.org/10.1109/TENCON.2007.4428840
Chung, Yi-Nung ; Chou, Cheng Nan ; Lan, Haw Chang ; Ho, Wen Hsin. / An automatic detection algorithm of MR images for knee pain problem. TENCON 2007 - 2007 IEEE Region 10 Conference. 2007. (IEEE Region 10 Annual International Conference, Proceedings/TENCON).
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title = "An automatic detection algorithm of MR images for knee pain problem",
abstract = "Anterior knee pain (AKP) is a common pathological condition. The problem causing knee pain is the abnormal patellar tracking mechanism. Kinematic approaches using MR images have been regarded of more accuracy in knee pain detection than stationary approaches. This paper proposes an automatic diagnosis based kinematic patellar tracking for AKP detection. The kinematic patellar tracking uses a hybrid approach for extracting knee organs, where an edge-constrained wavelet enhancement followed by moment preserving segmentation is employed for conquering the soft tissue adhesion for extracting the femur and tibia from axial MR images, and a sliding window based moment preserving for resolving the segmentation difficulty associated with intensity non-uniformity in saggital MR images. The experiment results demonstrate the prominent of the calculated inclination angles in detecting AKP.",
author = "Yi-Nung Chung and Chou, {Cheng Nan} and Lan, {Haw Chang} and Ho, {Wen Hsin}",
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Chung, Y-N, Chou, CN, Lan, HC & Ho, WH 2007, An automatic detection algorithm of MR images for knee pain problem. in TENCON 2007 - 2007 IEEE Region 10 Conference., 4428840, IEEE Region 10 Annual International Conference, Proceedings/TENCON, IEEE Region 10 Conference, TENCON 2007, Taipei, Taiwan, 07-10-30. https://doi.org/10.1109/TENCON.2007.4428840

An automatic detection algorithm of MR images for knee pain problem. / Chung, Yi-Nung; Chou, Cheng Nan; Lan, Haw Chang; Ho, Wen Hsin.

TENCON 2007 - 2007 IEEE Region 10 Conference. 2007. 4428840 (IEEE Region 10 Annual International Conference, Proceedings/TENCON).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - Anterior knee pain (AKP) is a common pathological condition. The problem causing knee pain is the abnormal patellar tracking mechanism. Kinematic approaches using MR images have been regarded of more accuracy in knee pain detection than stationary approaches. This paper proposes an automatic diagnosis based kinematic patellar tracking for AKP detection. The kinematic patellar tracking uses a hybrid approach for extracting knee organs, where an edge-constrained wavelet enhancement followed by moment preserving segmentation is employed for conquering the soft tissue adhesion for extracting the femur and tibia from axial MR images, and a sliding window based moment preserving for resolving the segmentation difficulty associated with intensity non-uniformity in saggital MR images. The experiment results demonstrate the prominent of the calculated inclination angles in detecting AKP.

AB - Anterior knee pain (AKP) is a common pathological condition. The problem causing knee pain is the abnormal patellar tracking mechanism. Kinematic approaches using MR images have been regarded of more accuracy in knee pain detection than stationary approaches. This paper proposes an automatic diagnosis based kinematic patellar tracking for AKP detection. The kinematic patellar tracking uses a hybrid approach for extracting knee organs, where an edge-constrained wavelet enhancement followed by moment preserving segmentation is employed for conquering the soft tissue adhesion for extracting the femur and tibia from axial MR images, and a sliding window based moment preserving for resolving the segmentation difficulty associated with intensity non-uniformity in saggital MR images. The experiment results demonstrate the prominent of the calculated inclination angles in detecting AKP.

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Chung Y-N, Chou CN, Lan HC, Ho WH. An automatic detection algorithm of MR images for knee pain problem. In TENCON 2007 - 2007 IEEE Region 10 Conference. 2007. 4428840. (IEEE Region 10 Annual International Conference, Proceedings/TENCON). https://doi.org/10.1109/TENCON.2007.4428840