Model-Based Approach to Recognize the Rectus Abdominis Muscle in CT Images

Naoki KAMIYA; Xiangrong ZHOU; Huayue CHEN; Chisako MURAMATSU; Takeshi HARA; Hiroshi FUJITA

doi:10.1587/transinf.E96.D.869

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Model-Based Approach to Recognize the Rectus Abdominis Muscle in CT Images

Naoki KAMIYA, Xiangrong ZHOU, Huayue CHEN, Chisako MURAMATSU, Takeshi HARA, Hiroshi FUJITA

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Our purpose in this study is to develop a scheme to segment the rectus abdominis muscle region in X-ray CT images. We propose a new muscle recognition method based on the shape model. In this method, three steps are included in the segmentation process. The first is to generate a shape model for representing the rectus abdominis muscle. The second is to recognize anatomical feature points corresponding to the origin and insertion of the muscle, and the third is to segment the rectus abdominis muscles using the shape model. We generated the shape model from 20 CT cases and tested the model to recognize the muscle in 10 other CT cases. The average value of the Jaccard similarity coefficient (JSC) between the manually and automatically segmented regions was 0.843. The results suggest the validity of the model-based segmentation for the rectus abdominis muscle.

Publication: IEICE TRANSACTIONS on Information Vol.E96-D No.4 pp.869-871

Publication Date: 2013/04/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E96.D.869

Type of Manuscript: Special Section LETTER (Special Section on Medical Imaging)

Category: Medical Image Processing

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Naoki KAMIYA, Xiangrong ZHOU, Huayue CHEN, Chisako MURAMATSU, Takeshi HARA, Hiroshi FUJITA, "Model-Based Approach to Recognize the Rectus Abdominis Muscle in CT Images" in IEICE TRANSACTIONS on Information, vol. E96-D, no. 4, pp. 869-871, April 2013, doi: 10.1587/transinf.E96.D.869.
Abstract: Our purpose in this study is to develop a scheme to segment the rectus abdominis muscle region in X-ray CT images. We propose a new muscle recognition method based on the shape model. In this method, three steps are included in the segmentation process. The first is to generate a shape model for representing the rectus abdominis muscle. The second is to recognize anatomical feature points corresponding to the origin and insertion of the muscle, and the third is to segment the rectus abdominis muscles using the shape model. We generated the shape model from 20 CT cases and tested the model to recognize the muscle in 10 other CT cases. The average value of the Jaccard similarity coefficient (JSC) between the manually and automatically segmented regions was 0.843. The results suggest the validity of the model-based segmentation for the rectus abdominis muscle.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E96.D.869/_p

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@ARTICLE{e96-d_4_869,
author={Naoki KAMIYA, Xiangrong ZHOU, Huayue CHEN, Chisako MURAMATSU, Takeshi HARA, Hiroshi FUJITA, },
journal={IEICE TRANSACTIONS on Information},
title={Model-Based Approach to Recognize the Rectus Abdominis Muscle in CT Images},
year={2013},
volume={E96-D},
number={4},
pages={869-871},
abstract={Our purpose in this study is to develop a scheme to segment the rectus abdominis muscle region in X-ray CT images. We propose a new muscle recognition method based on the shape model. In this method, three steps are included in the segmentation process. The first is to generate a shape model for representing the rectus abdominis muscle. The second is to recognize anatomical feature points corresponding to the origin and insertion of the muscle, and the third is to segment the rectus abdominis muscles using the shape model. We generated the shape model from 20 CT cases and tested the model to recognize the muscle in 10 other CT cases. The average value of the Jaccard similarity coefficient (JSC) between the manually and automatically segmented regions was 0.843. The results suggest the validity of the model-based segmentation for the rectus abdominis muscle.},
keywords={},
doi={10.1587/transinf.E96.D.869},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - Model-Based Approach to Recognize the Rectus Abdominis Muscle in CT Images
T2 - IEICE TRANSACTIONS on Information
SP - 869
EP - 871
AU - Naoki KAMIYA
AU - Xiangrong ZHOU
AU - Huayue CHEN
AU - Chisako MURAMATSU
AU - Takeshi HARA
AU - Hiroshi FUJITA
PY - 2013
DO - 10.1587/transinf.E96.D.869
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2013
AB - Our purpose in this study is to develop a scheme to segment the rectus abdominis muscle region in X-ray CT images. We propose a new muscle recognition method based on the shape model. In this method, three steps are included in the segmentation process. The first is to generate a shape model for representing the rectus abdominis muscle. The second is to recognize anatomical feature points corresponding to the origin and insertion of the muscle, and the third is to segment the rectus abdominis muscles using the shape model. We generated the shape model from 20 CT cases and tested the model to recognize the muscle in 10 other CT cases. The average value of the Jaccard similarity coefficient (JSC) between the manually and automatically segmented regions was 0.843. The results suggest the validity of the model-based segmentation for the rectus abdominis muscle.
ER -