東華大學圖書館 |

語系: 繁體中文

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

Machine Learning Guided Advanced Image Reconstruction in Photo Magnetic Imaging.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning Guided Advanced Image Reconstruction in Photo Magnetic Imaging./
作者:	Saraswatula, Janaki Sankirthana.
面頁冊數:	1 online resource (73 pages)
附註:	Source: Masters Abstracts International, Volume: 84-10.
Contained By:	Masters Abstracts International84-10.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30313215click for full text (PQDT)
ISBN:	9798379421021

Machine Learning Guided Advanced Image Reconstruction in Photo Magnetic Imaging.
Saraswatula, Janaki Sankirthana.

Machine Learning Guided Advanced Image Reconstruction in Photo Magnetic Imaging. - 1 online resource (73 pages)

Source: Masters Abstracts International, Volume: 84-10.

Thesis (M.S.)--University of California, Irvine, 2023.

Includes bibliographical references

Photo Magnetic Imaging (PMI) is a novel laser-based optical imaging technique. PMI is used to identify diseased tissue based on its endogenous tissue contrast or using disease-targeting exogenous optical contrast agents. It was first developed at the Center for Onco Functional Imaging at the University of California, Irvine. Optical imaging techniques generally suffer from poor spatial resolution due to the high scattering of optical photons in tissue. PMI attempts to provide quantitatively accurate optical images with high spatial resolution. In this study, phantoms mimicking optical properties of tissue are utilized to explore the use of Machine learning techniques and Finite Element Methods to drive an AI-based reconstruction of PMI images. Our proposed methodology consists of hybrid deep learning and a machine learning pipeline that first identifies the inclusions in the phantoms representing cancerous lesions and finally delineates their boundaries. Our method was tested on a variety of inclusions' sizes, locations, and absorption coefficients and demonstrated high precision (above 95%) and Intersection Over Union Accuracies (above 85%) for the realizable test cases.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379421021Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Deep learningIndex Terms--Genre/Form:

542853
Electronic books.

Machine Learning Guided Advanced Image Reconstruction in Photo Magnetic Imaging.
LDR:02555nmm a2200397K 4500 001 2362677
005 20231102122756.5
006 m o d
007 cr mn ---uuuuu
008 241011s2023 xx obm 000 0 eng d
020 $a 9798379421021
035 $a (MiAaPQ)AAI30313215
035 $a AAI30313215
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Saraswatula, Janaki Sankirthana. $3 3703419
245 1 0 $a Machine Learning Guided Advanced Image Reconstruction in Photo Magnetic Imaging.
264 0 $c 2023
300 $a 1 online resource (73 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 84-10.
500 $a Advisor: Gulsen, Gultekin.
502 $a Thesis (M.S.)--University of California, Irvine, 2023.
504 $a Includes bibliographical references
520 $a Photo Magnetic Imaging (PMI) is a novel laser-based optical imaging technique. PMI is used to identify diseased tissue based on its endogenous tissue contrast or using disease-targeting exogenous optical contrast agents. It was first developed at the Center for Onco Functional Imaging at the University of California, Irvine. Optical imaging techniques generally suffer from poor spatial resolution due to the high scattering of optical photons in tissue. PMI attempts to provide quantitatively accurate optical images with high spatial resolution. In this study, phantoms mimicking optical properties of tissue are utilized to explore the use of Machine learning techniques and Finite Element Methods to drive an AI-based reconstruction of PMI images. Our proposed methodology consists of hybrid deep learning and a machine learning pipeline that first identifies the inclusions in the phantoms representing cancerous lesions and finally delineates their boundaries. Our method was tested on a variety of inclusions' sizes, locations, and absorption coefficients and demonstrated high precision (above 95%) and Intersection Over Union Accuracies (above 85%) for the realizable test cases.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 649834
650 4 $a Computer engineering. $3 621879
650 4 $a Optics. $3 517925
653 $a Deep learning
653 $a Image processing
653 $a Machine learning
653 $a Medical imaging
653 $a Photo magnetic imaging
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0544
690 $a 0752
690 $a 0464
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a University of California, Irvine. $b Electrical and Computer Engineering. $3 2100610
773 0 $t Masters Abstracts International $g 84-10.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30313215 $z click for full text (PQDT)