語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
FindBook
Google Book
Amazon
博客來
Statistical Machine Learning for Breast Cancer Detection with Terahertz Imaging.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Statistical Machine Learning for Breast Cancer Detection with Terahertz Imaging./
作者:
Chavez Esparza, Tanny Andrea.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:
128 p.
附註:
Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
Contained By:
Dissertations Abstracts International82-12B.
標題:
Electrical engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28492753
ISBN:
9798516055867
Statistical Machine Learning for Breast Cancer Detection with Terahertz Imaging.
Chavez Esparza, Tanny Andrea.
Statistical Machine Learning for Breast Cancer Detection with Terahertz Imaging.
- Ann Arbor : ProQuest Dissertations & Theses, 2021 - 128 p.
Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
Thesis (Ph.D.)--University of Arkansas, 2021.
This item must not be sold to any third party vendors.
Breast conserving surgery (BCS) is a common breast cancer treatment option, in which the cancerous tissue is excised while leaving most of the healthy breast tissue intact. The lack of in-situ margin evaluation unfortunately results in a re-excision rate of 20-30% for this type of procedure. This study aims to design statistical and machine learning segmentation algorithms for the detection of breast cancer in BCS by using terahertz (THz) imaging. Given the material characterization properties of the non-ionizing radiation in the THz range, we intend to employ the responses from the THz system to identify healthy and cancerous breast tissue in BCS samples. In particular, this dissertation covers the description of four segmentation algorithms for the detection of breast cancer in THz imaging. We first explore the performance of one-dimensional (1D) Gaussian mixture and t-mixture models with Markov chain Monte Carlo (MCMC). Second, we propose a novel low-dimension ordered orthogonal projection (LOOP) algorithm for the dimension reduction of the THz information through a modified Gram-Schmidt process. Once the key features within the THz waveform have been detected by LOOP, the segmentation algorithm employs a multivariate Gaussian mixture model with MCMC and expectation maximization (EM). Third, we explore the spatial information of each pixel within the THz image through a Markov random field (MRF) approach. Finally, we introduce a supervised multinomial probit regression algorithm with polynomial and kernel data representations. For evaluation purposes, this study makes use of fresh and formalin-fixed paraffin-embedded (FFPE) heterogeneous human and mice tissue models for the quantitative assessment of the segmentation performance in terms of receiver operating characteristics (ROC) curves. Overall, the experimental results demonstrate that the proposed approaches represent a promising technique for tissue segmentation within THz images of freshly excised breast cancer samples.
ISBN: 9798516055867Subjects--Topical Terms:
649834
Electrical engineering.
Subjects--Index Terms:
Image segmentation
Statistical Machine Learning for Breast Cancer Detection with Terahertz Imaging.
LDR
:03257nmm a2200397 4500
001
2349534
005
20230509091106.5
006
m o d
007
cr#unu||||||||
008
241004s2021 ||||||||||||||||| ||eng d
020
$a
9798516055867
035
$a
(MiAaPQ)AAI28492753
035
$a
AAI28492753
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Chavez Esparza, Tanny Andrea.
$0
(orcid)0000-0001-9317-2896
$3
3688944
245
1 0
$a
Statistical Machine Learning for Breast Cancer Detection with Terahertz Imaging.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2021
300
$a
128 p.
500
$a
Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
500
$a
Advisor: Wu, Jingxian.
502
$a
Thesis (Ph.D.)--University of Arkansas, 2021.
506
$a
This item must not be sold to any third party vendors.
520
$a
Breast conserving surgery (BCS) is a common breast cancer treatment option, in which the cancerous tissue is excised while leaving most of the healthy breast tissue intact. The lack of in-situ margin evaluation unfortunately results in a re-excision rate of 20-30% for this type of procedure. This study aims to design statistical and machine learning segmentation algorithms for the detection of breast cancer in BCS by using terahertz (THz) imaging. Given the material characterization properties of the non-ionizing radiation in the THz range, we intend to employ the responses from the THz system to identify healthy and cancerous breast tissue in BCS samples. In particular, this dissertation covers the description of four segmentation algorithms for the detection of breast cancer in THz imaging. We first explore the performance of one-dimensional (1D) Gaussian mixture and t-mixture models with Markov chain Monte Carlo (MCMC). Second, we propose a novel low-dimension ordered orthogonal projection (LOOP) algorithm for the dimension reduction of the THz information through a modified Gram-Schmidt process. Once the key features within the THz waveform have been detected by LOOP, the segmentation algorithm employs a multivariate Gaussian mixture model with MCMC and expectation maximization (EM). Third, we explore the spatial information of each pixel within the THz image through a Markov random field (MRF) approach. Finally, we introduce a supervised multinomial probit regression algorithm with polynomial and kernel data representations. For evaluation purposes, this study makes use of fresh and formalin-fixed paraffin-embedded (FFPE) heterogeneous human and mice tissue models for the quantitative assessment of the segmentation performance in terms of receiver operating characteristics (ROC) curves. Overall, the experimental results demonstrate that the proposed approaches represent a promising technique for tissue segmentation within THz images of freshly excised breast cancer samples.
590
$a
School code: 0011.
650
4
$a
Electrical engineering.
$3
649834
650
4
$a
Oncology.
$3
751006
650
4
$a
Biostatistics.
$3
1002712
650
4
$a
Medical imaging.
$3
3172799
653
$a
Image segmentation
653
$a
Statistical inference
653
$a
Breast conserving surgery
653
$a
Receiver operating characteristics
690
$a
0544
690
$a
0992
690
$a
0574
690
$a
0308
710
2
$a
University of Arkansas.
$b
Electrical Engineering.
$3
2103922
773
0
$t
Dissertations Abstracts International
$g
82-12B.
790
$a
0011
791
$a
Ph.D.
792
$a
2021
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28492753
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9471972
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入