메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술대회자료
저자정보
Tatsushi Tokuyasu (Oita National College of Technology) Takashi Shuto (Oita National College of Technology) Kenji Yufu (Oita National College of Technology) Shotaro Kanao (Graduate School of Medicine Kyoto University) Akira Marui (Graduate School of Medicine Kyoto University) Masashi Komeda (Nagoya Heart Center)
저널정보
제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2010
발행연도
2010.10
수록면
2,090 - 2,093 (4page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
Computer-Aided Diagnosis (CAD) system that helps medical staffs to diagnose patient"s disease conditions has been used in a variety fields of medicine. For cardiovascular surgery, radiologists manually construct 3-D volume model of patient organ and provide this information to cardiovascular surgeons, therefore automation technique for image processing of building patient 3-D volume model is highly requested from clinical site. The 3-D volume model is used in not only diagnosing patient disease condition, but also making a surgical plan before an operation. In the case of using CAD system for a cardiovascular disease patient, computed tomography angiography (CTA) has been used as the source data that clearly indicates the region of blood flow on the image due to contrast agent. However, sufficient information for the diagnosis is not obtained from CTA, because the regions of aneurysm and aortic wall tissue can not distinguished correctly even using the latest CAD system. Then, this study proposes Fuzzy-based region growing method that enables a computer to have the ability of reading radiogram. We focused on the skill of reading radiogram of experienced doctors, because they know the boundary line between aneurysm and aortic wall tissue on CTA image. Hence, Fuzzy inference has been employed to express doctor"s skill of reading radiogram and used as the growing criteria. The proposed method is applied to one patient CTA data and its result is shown and discussed in this paper.

목차

Abstract
INTRODUCTION
2. MATERIALS AND METHODS
3. RESULT
4. DISCUSSIONS
REFERENCES

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2014-569-000940951