東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Correlative analysis of breast lesio...

Yuan, Yading.

Linked to FindBook

Google Book

Amazon

博客來

Correlative analysis of breast lesions on full-field digital mammography and magnetic resonance imaging.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Correlative analysis of breast lesions on full-field digital mammography and magnetic resonance imaging./
Author:	Yuan, Yading.
Description:	154 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-07, Section: B, page: 4319.
Contained By:	Dissertation Abstracts International71-07B.
Subject:	Health Sciences, Radiology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3408613
ISBN:	9781124049533

Correlative analysis of breast lesions on full-field digital mammography and magnetic resonance imaging.
Yuan, Yading.

Correlative analysis of breast lesions on full-field digital mammography and magnetic resonance imaging. - 154 p.

Source: Dissertation Abstracts International, Volume: 71-07, Section: B, page: 4319.

Thesis (Ph.D.)--The University of Chicago, 2010.

Multi-modality imaging techniques are increasingly being applied in clinical practice to improve the accuracy with which breast cancer can be diagnosed. However, interpreting images from different modalities is not trivial as different images of the same lesion may exhibit different physical lesion attributes, and currently the various image modality acquisitions are performed under different breast positioning protocols.

ISBN: 9781124049533Subjects--Topical Terms:

1019076
Health Sciences, Radiology.

Correlative analysis of breast lesions on full-field digital mammography and magnetic resonance imaging.
LDR:04285nam 2200361 4500 001 1392571
005 20110218114616.5
008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124049533
035 $a (UMI)AAI3408613
035 $a AAI3408613
040 $a UMI $c UMI
100 1 $a Yuan, Yading. $3 1671029
245 1 0 $a Correlative analysis of breast lesions on full-field digital mammography and magnetic resonance imaging.
300 $a 154 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-07, Section: B, page: 4319.
500 $a Adviser: Maryellen L. Giger.
502 $a Thesis (Ph.D.)--The University of Chicago, 2010.
520 $a Multi-modality imaging techniques are increasingly being applied in clinical practice to improve the accuracy with which breast cancer can be diagnosed. However, interpreting images from different modalities is not trivial as different images of the same lesion may exhibit different physical lesion attributes, and currently the various image modality acquisitions are performed under different breast positioning protocols.
520 $a The general objective of this research is to investigate computerized correlative feature analysis (CFA) methods for integrating information from full-field digital mammographic (FFDM) images and dynamic contrast-enhanced magnetic resonance (DCE-MR) images by taking advantage of the information from different imaging modalities, and thus improving the diagnostic ability of computer-aided diagnosis (CADx) in breast cancer workup. The main hypothesis to be tested is that by incorporating correlative feature analysis in CADx, one can achieve an accurate and efficient discrimination between corresponding and non-corresponding lesion pairs, and subsequently improve performance in the estimation of computer-estimated probabilities of malignancy.
520 $a The main contributions of this research work are summarized as follows. (1) A novel active-contour model based algorithm was developed for lesion segmentation on mammograms. This new algorithm yielded a statistically improved segmentation performance as compared to previously developed methods: a region-growing method and a radial gradient index (RGI) based method. (2) A computerized feature-based, supervised-learning driven CFA method was investigated to identify corresponding lesions in different mammographic views. The performance obtained by combining multiple features was found to be statistically better than the use of a distance feature alone, and robust across different mammographic view combinations. (3) A multi-modality CADx method that automatically selects and combines discriminative information from both mammography and DCE-MR imaging was studied, and yielded a statistically improved diagnostic performance as compared to the use of single-modality CADx. (4) The CFA method was successfully generalized to the task of differentiating between corresponding and non-corresponding lesions seen in mammographic images and DCE-MR images. Furthermore, multi-modality CADx, which incorporated CFA, was found to potentially provide improved diagnostic accuracy as compared to both single-modality CADx and to multi-modality CADx that erroneously includes non-corresponding lesion pairs.
520 $a The results affirm the main hypothesis of this research work. With the increasing role of multi-modality imaging techniques in the clinical evaluation, computerized analysis, integration, and interpretation of the image data become more essential for breast cancer diagnosis. The significance of this research is that it provides an automated, effective and efficient scheme that has potential to help radiologists in achieving an improved correlation and characterization of breast lesions with multi-modality imaging techniques.
590 $a School code: 0330.
650 4 $a Health Sciences, Radiology. $3 1019076
650 4 $a Physics, Radiation. $3 1019212
690 $a 0574
690 $a 0756
710 2 $a The University of Chicago. $b Medical Physics. $3 1671030
773 0 $t Dissertation Abstracts International $g 71-07B.
790 1 0 $a Giger, Maryellen L., $e advisor
790 1 0 $a Jiang, Yulei $e committee member
790 1 0 $a Metz, Charles E. $e committee member
790 1 0 $a Sennett, Charlene A. $e committee member
790 1 0 $a Suzuki, Kunji $e committee member
790 $a 0330
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3408613