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Automated Analysis of Quantitative Image Biomarkers from Low-Dose Chest CT Scans
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Automated Analysis of Quantitative Image Biomarkers from Low-Dose Chest CT Scans. |
| 개인저자 | Liu, Shuang. |
| 단체저자명 | Cornell University. Electrical & Computer Engineering. |
| 발행사항 | [S.l.]: Cornell University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 170 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438343542 |
| 학위논문주기 | Thesis (Ph.D.)--Cornell University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Anthony P. Reeves. |
| 요약 | A quantitative imaging biomarker is a quantitatively measured characteristic derived from medical images, which serves as cost-effective and noninvasive tools for patient health assessment, including diagnosis and periodic screening of disease, |
| 요약 | This dissertation presents an automated framework for quantitative image biomarker measurement and evaluation from the low-dose chest CT (LDCT) scans that are acquired during the annual lung cancer screening. Four categories of quantitative imag |
| 요약 | In conclusion, with the recent large-scale implementation of annual lung cancer screening in the US using LDCT, great potential emerges for the concurrent extraction of quantitative image biomarkers from different regions in the chest, which are |
| 일반주제명 | Computer engineering. Computer science. Electrical engineering. Artificial intelligence. Medical imaging. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
