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

Image Analysis by Projections in 3D ...

Rasul, Raisa Binte.

Linked to FindBook

Google Book

Amazon

博客來

Image Analysis by Projections in 3D Images With Applications in Medical Image Analysis and Precise Image Measurement.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Image Analysis by Projections in 3D Images With Applications in Medical Image Analysis and Precise Image Measurement./
Author:	Rasul, Raisa Binte.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	132 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
Contained By:	Dissertations Abstracts International85-07B.
Subject:	Biomedical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30816031
ISBN:	9798381413908

Image Analysis by Projections in 3D Images With Applications in Medical Image Analysis and Precise Image Measurement.
Rasul, Raisa Binte.

Image Analysis by Projections in 3D Images With Applications in Medical Image Analysis and Precise Image Measurement. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 132 p.

Source: Dissertations Abstracts International, Volume: 85-07, Section: B.

Thesis (Ph.D.)--Cornell University, 2023.

Standard image analysis is typically performed manually, which is often time-inefficient and in many cases imprecise. This is an issue particularly in the field of radiology where clinical decisions need to be made both quickly and accurately based on medical image analysis results. Therefore, automated image analysis has been a field of high interest over the past several decades. In this thesis, work is described in which different automated methods of image analysis are used to quantify automated image measurement precision, to segment the amount of calcium present in the coronary arteries, and to identify locations of asbestos burden along the pleural surface of the lung.In the first section a novel method of quantifying the dynamic measurement precision of an automated system measuring spherical, soot-producing fuel droplets undergoing combustion is presented. This automated measuring system utilizes least-squares circle/ellipse fitting to detect and measure the diameter of these droplets even when partially occluded by soot. From this, quality metrics such as gradient intensity and goodness of fit were identified, and synthetic image simulation was used to correlate these quality metrics with dynamic measurement precision. The final measurement system was able to achieve measurement precision of ± 0.2 pixels in cases of low occlusion. This work demonstrates that an automated image analysis system is capable of performing measurements with dynamic subpixel precision, far higher than what is possible with human annotations.In the second chapter, an automated system for detecting and quantifying coronary artery calcium (CAC) in cardiac computed tomography (CT) scans is described. This work utilized images from the Multi-Ethnic Study of Atherosclerosis (MESA) dataset along with radiologist-labeled pixel ground truths for each scan. This algorithm utilizes traditional computer vision techniques to achieve this goal. Within this, a heart segmentation was developed that enabled a general localization of the coronary arteries by mapping a probability map onto a heart surface mesh. This was used to identify candidate CAC plaques. Features were then extracted, and a random forest classifier was used to determine true and false CAC. This method achieved a Pearson correlation of 0.98, an average Dice score of 0.85, and a risk-category accuracy of 0.87. This work was able to achieve results similar to existing state-of-the-art methods.In the final chapter, an algorithm for detecting the presence of asbestos exposure in full-chest CT scans is presented. This work focused on patients that live in Libby, Montana where a significant percent of the population has been exposed to asbestos originating from nearby vermiculite mines. Asbestos leads to a multitude of presentations on the lung surface including the thickening of the pleural membrane and manifestation of plaques. Specifically, in Libby cases, patients exhibit a specific type of pleural thickening, like lamellar pleural thickening, on chest CT scans that is difficult to detect by radiologist. Therefore, to detect this thickening and other disease conditions, the lung was segmented, and its surface was sampled in an orthogonal fashion to obtain subpixel intensity profiles along the edge of the lung. Features from these profiles were then used to identify regions of interest where there was likely asbestos exposure. This method was evaluated on healthy and diseased Libby cases as well as an external dataset [Lung Image Database Consortium (LIDC)]. The fraction of the lung surface identified as diseased by the algorithm was significantly higher in the diseased cases when compared to the healthy and LIDC cases. This showed that the methods developed for identifying pleural thickening in Libby cases was successfully achieved.

ISBN: 9798381413908Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

3D images

Image Analysis by Projections in 3D Images With Applications in Medical Image Analysis and Precise Image Measurement.
LDR:05053nmm a2200397 4500 001 2403621
005 20241118135851.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798381413908
035 $a (MiAaPQ)AAI30816031
035 $a AAI30816031
040 $a MiAaPQ $c MiAaPQ
100 1 $a Rasul, Raisa Binte. $0 (orcid)0000-0001-5815-9151 $3 3773893
245 1 0 $a Image Analysis by Projections in 3D Images With Applications in Medical Image Analysis and Precise Image Measurement.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 132 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
500 $a Advisor: Reeves, Anthony.
502 $a Thesis (Ph.D.)--Cornell University, 2023.
520 $a Standard image analysis is typically performed manually, which is often time-inefficient and in many cases imprecise. This is an issue particularly in the field of radiology where clinical decisions need to be made both quickly and accurately based on medical image analysis results. Therefore, automated image analysis has been a field of high interest over the past several decades. In this thesis, work is described in which different automated methods of image analysis are used to quantify automated image measurement precision, to segment the amount of calcium present in the coronary arteries, and to identify locations of asbestos burden along the pleural surface of the lung.In the first section a novel method of quantifying the dynamic measurement precision of an automated system measuring spherical, soot-producing fuel droplets undergoing combustion is presented. This automated measuring system utilizes least-squares circle/ellipse fitting to detect and measure the diameter of these droplets even when partially occluded by soot. From this, quality metrics such as gradient intensity and goodness of fit were identified, and synthetic image simulation was used to correlate these quality metrics with dynamic measurement precision. The final measurement system was able to achieve measurement precision of ± 0.2 pixels in cases of low occlusion. This work demonstrates that an automated image analysis system is capable of performing measurements with dynamic subpixel precision, far higher than what is possible with human annotations.In the second chapter, an automated system for detecting and quantifying coronary artery calcium (CAC) in cardiac computed tomography (CT) scans is described. This work utilized images from the Multi-Ethnic Study of Atherosclerosis (MESA) dataset along with radiologist-labeled pixel ground truths for each scan. This algorithm utilizes traditional computer vision techniques to achieve this goal. Within this, a heart segmentation was developed that enabled a general localization of the coronary arteries by mapping a probability map onto a heart surface mesh. This was used to identify candidate CAC plaques. Features were then extracted, and a random forest classifier was used to determine true and false CAC. This method achieved a Pearson correlation of 0.98, an average Dice score of 0.85, and a risk-category accuracy of 0.87. This work was able to achieve results similar to existing state-of-the-art methods.In the final chapter, an algorithm for detecting the presence of asbestos exposure in full-chest CT scans is presented. This work focused on patients that live in Libby, Montana where a significant percent of the population has been exposed to asbestos originating from nearby vermiculite mines. Asbestos leads to a multitude of presentations on the lung surface including the thickening of the pleural membrane and manifestation of plaques. Specifically, in Libby cases, patients exhibit a specific type of pleural thickening, like lamellar pleural thickening, on chest CT scans that is difficult to detect by radiologist. Therefore, to detect this thickening and other disease conditions, the lung was segmented, and its surface was sampled in an orthogonal fashion to obtain subpixel intensity profiles along the edge of the lung. Features from these profiles were then used to identify regions of interest where there was likely asbestos exposure. This method was evaluated on healthy and diseased Libby cases as well as an external dataset [Lung Image Database Consortium (LIDC)]. The fraction of the lung surface identified as diseased by the algorithm was significantly higher in the diseased cases when compared to the healthy and LIDC cases. This showed that the methods developed for identifying pleural thickening in Libby cases was successfully achieved.
590 $a School code: 0058.
650 4 $a Biomedical engineering. $3 535387
650 4 $a Medical imaging. $3 3172799
650 4 $a Bioengineering. $3 657580
653 $a 3D images
653 $a Image measurement
653 $a Medical image segmentation
653 $a Polygon surface mesh
653 $a CT scans
653 $a Radiology
690 $a 0541
690 $a 0202
690 $a 0574
710 2 $a Cornell University. $b Biomedical Engineering. $3 3174073
773 0 $t Dissertations Abstracts International $g 85-07B.
790 $a 0058
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30816031