The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
활성 형상 모델(ASM)은 방사선 영상을 위한 자동화된 뼈 분할 접근법에 널리 채택되었습니다. 원위 요골의 방사선 영상에서 뼈 근처에 여러 개의 가장자리가 종종 관찰되는데, 이는 일반적으로 얇은 연조직의 존재로 인해 발생합니다. 여러 모서리가 있으면 ASM을 사용하여 원위 요골을 분할할 때 분할 정확도가 감소합니다. 본 논문에서는 수정된 버전의 ASM을 사용하여 분할 오류 수를 줄이는 향상된 원위 요골 분할 방법을 제안합니다. 분할 오류를 완화하기 위해 제안된 방법은 이중 에너지 X선 흡수계(DXA)를 사용하여 뼈 가장자리의 존재를 강조하고 연조직 가장자리의 존재를 경시합니다. 제안된 분할 방법의 유효성을 검증하기 위해 원위 요골 환자 영상 30개를 대상으로 실험을 수행하였다. 사용된 이미지에 대해 제안하는 방법은 ASM 기반 분할에 비해 분할 정확도를 47.4%(0.974mm에서 0.512mm로) 향상시켰다.
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Sihyoung LEE, Sunil CHO, Yong Man RO, "Enhanced Distal Radius Segmentation in DXA Using Modified ASM" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 2, pp. 363-370, February 2011, doi: 10.1587/transinf.E94.D.363.
Abstract: The active shape model (ASM) has been widely adopted by automated bone segmentation approaches for radiographic images. In radiographic images of the distal radius, multiple edges are often observed in the near vicinity of the bone, typically caused by the presence of thin soft tissue. The presence of multiple edges decreases the segmentation accuracy when segmenting the distal radius using ASM. In this paper, we propose an enhanced distal radius segmentation method that makes use of a modified version of ASM, reducing the number of segmentation errors. To mitigate segmentation errors, the proposed method emphasizes the presence of the bone edge and downplays the presence of a soft tissue edge by making use of Dual energy X-ray absorptiometry (DXA). To verify the effectiveness of the proposed segmentation method, experiments were performed with 30 distal radius patient images. For the images used, compared to ASM-based segmentation, the proposed method improves the segmentation accuracy with 47.4% (from 0.974 mm to 0.512 mm).
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.363/_p
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@ARTICLE{e94-d_2_363,
author={Sihyoung LEE, Sunil CHO, Yong Man RO, },
journal={IEICE TRANSACTIONS on Information},
title={Enhanced Distal Radius Segmentation in DXA Using Modified ASM},
year={2011},
volume={E94-D},
number={2},
pages={363-370},
abstract={The active shape model (ASM) has been widely adopted by automated bone segmentation approaches for radiographic images. In radiographic images of the distal radius, multiple edges are often observed in the near vicinity of the bone, typically caused by the presence of thin soft tissue. The presence of multiple edges decreases the segmentation accuracy when segmenting the distal radius using ASM. In this paper, we propose an enhanced distal radius segmentation method that makes use of a modified version of ASM, reducing the number of segmentation errors. To mitigate segmentation errors, the proposed method emphasizes the presence of the bone edge and downplays the presence of a soft tissue edge by making use of Dual energy X-ray absorptiometry (DXA). To verify the effectiveness of the proposed segmentation method, experiments were performed with 30 distal radius patient images. For the images used, compared to ASM-based segmentation, the proposed method improves the segmentation accuracy with 47.4% (from 0.974 mm to 0.512 mm).},
keywords={},
doi={10.1587/transinf.E94.D.363},
ISSN={1745-1361},
month={February},}
부
TY - JOUR
TI - Enhanced Distal Radius Segmentation in DXA Using Modified ASM
T2 - IEICE TRANSACTIONS on Information
SP - 363
EP - 370
AU - Sihyoung LEE
AU - Sunil CHO
AU - Yong Man RO
PY - 2011
DO - 10.1587/transinf.E94.D.363
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2011
AB - The active shape model (ASM) has been widely adopted by automated bone segmentation approaches for radiographic images. In radiographic images of the distal radius, multiple edges are often observed in the near vicinity of the bone, typically caused by the presence of thin soft tissue. The presence of multiple edges decreases the segmentation accuracy when segmenting the distal radius using ASM. In this paper, we propose an enhanced distal radius segmentation method that makes use of a modified version of ASM, reducing the number of segmentation errors. To mitigate segmentation errors, the proposed method emphasizes the presence of the bone edge and downplays the presence of a soft tissue edge by making use of Dual energy X-ray absorptiometry (DXA). To verify the effectiveness of the proposed segmentation method, experiments were performed with 30 distal radius patient images. For the images used, compared to ASM-based segmentation, the proposed method improves the segmentation accuracy with 47.4% (from 0.974 mm to 0.512 mm).
ER -