Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Classification based Knee Bone Detection using Context Information

문맥 정보를 이용한 분류 기반 무릎 뼈 검출 기법

  • Shin, Seungyeon (Automation and Systems Research Institute, Department of Electrical and Computer Engineering, Seoul National University) ;
  • Park, Sanghyun (Automation and Systems Research Institute, Department of Electrical and Computer Engineering, Seoul National University) ;
  • Yun, Il Dong (School of Digital Information Engineering, Hankuk University of Foreign Studies) ;
  • Lee, Sang Uk (Automation and Systems Research Institute, Department of Electrical and Computer Engineering, Seoul National University)
  • 신승연 (서울대학교 전기.정보공학부 자동화연구소, 뉴미디어통신공동연구소) ;
  • 박상현 (서울대학교 전기.정보공학부 자동화연구소, 뉴미디어통신공동연구소) ;
  • 윤일동 (한국외국어대학교 용인캠퍼스 디지털정보공학과) ;
  • 이상욱 (서울대학교 전기.정보공학부 자동화연구소, 뉴미디어통신공동연구소)
  • Received : 2013.03.28
  • Accepted : 2013.05.28
  • Published : 2013.05.30
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Abstract

In this paper, we propose a method that automatically detects organs having similar appearances in medical images by learning both context and appearance features. Since only the appearance feature is used to learn the classifier in most existing detection methods, detection errors occur when the medical images include multiple organs having similar appearances. In the proposed method, based on the probabilities acquired by the appearance-based classifier, new classifier containing the context feature is created by iteratively learning the characteristics of probability distribution around the interest voxel. Furthermore, both the efficiency and the accuracy are improved through 'region based voting scheme' in test stage. To evaluate the performance of the proposed method, we detect femur and tibia which have similar appearance from SKI10 knee joint dataset. The proposed method outperformed the detection method only using appearance feature in aspect of overall detection performance.

본 논문에서는 영상 내의 문맥 특징(context feature)과 외형 특징(appearance feature)을 함께 학습함으로써 의료영상 내의 비슷한 외형 특징을 가지는 장기들을 자동으로 검출하는 기법을 제안한다. 기존 검출 기법들은 외형 특징 정보만을 학습하여 분류기(classifier)를 생성하였기 때문에 의료영상 내에 외형이 비슷한 장기들이 다수 포함되어 있는 경우 검출 오류가 발생하였다. 제안하는 기법은 외형 특징을 이용하여 학습된 분류기를 통해 얻은 확률 값들을 바탕으로 관심 복셀(voxel) 주변의 확률 분포 특징을 반복적으로 학습함으로써 문맥 정보를 포함하는 분류기를 생성한다. 또한, 실험 단계(test stage)에서 '지역 기반 투표 방식'(region based voting scheme)을 도입함으로써 효율성과 정확성을 향상시킨다. 제안하는 기법의 성능 평가를 위해 SKI10 무릎 관절 데이터 셋 내에서 외형 특징이 비슷한 대퇴골(femur)과 경골(tibia)을 검출하는 실험을 진행하였다. 실험 결과를 통해 제안하는 기법이 외형 특징만을 이용했던 검출 기법에 비해 개선된 검출 성능을 보이고 있음을 확인할 수 있었다.

Keywords

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