Multi-parameters ultrasound imaging for breast cancer diagnosis
博士 === 國立清華大學 === 生醫工程與環境科學系 === 101 === The primary goal of this work is to exploit the physical characteristics of feature type for better classifying breast tumors using ultrasound. The main topics can be divided to three certain parts. In chapter 2, ultrasound B-scan-based morphological and text...
Main Author: | |
---|---|
Other Authors: | |
Format: | Others |
Language: | en_US |
Published: |
2013
|
Online Access: | http://ndltd.ncl.edu.tw/handle/13837860823591031692 |
id |
ndltd-TW-101NTHU5810009 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-101NTHU58100092015-10-13T22:06:57Z http://ndltd.ncl.edu.tw/handle/13837860823591031692 Multi-parameters ultrasound imaging for breast cancer diagnosis 多功能參數於超音波乳癌診斷 廖尹吟 博士 國立清華大學 生醫工程與環境科學系 101 The primary goal of this work is to exploit the physical characteristics of feature type for better classifying breast tumors using ultrasound. The main topics can be divided to three certain parts. In chapter 2, ultrasound B-scan-based morphological and texture analysis and Nakagami parametric imaging were proposed to characterize breast tumors. These feature categories of ultrasound tissue characterization supplied information on different physical characteristics of breast tumors, by combining the above methods was expected to provide more clues for classifying breast tumors. The empirical results indicated that the combination of morphological-feature parameter (e.g., standard deviation of the shortest distance), texture feature (e.g., variance), and the Nakagami parameter resulted in the specificity and sensitivity both exceeded 88%, and the area under ROC curve of 0.95. In chapter 3, the feasibility of applying the elasticity imaging method to Nakagami imaging was investigated for visualizing the local redistributions of scatterers in a scattering medium with different stiffnesses. The preliminary results show the concept of the elasticity and scatterer characterizations being functionally complementary in classifying breast tumors. Consequently, the sequential Nakagami image frames obtained from different strain conditions would simply represent the relative tissue stiffness. In chapter 4, the use of a strain-compounding technique with Nakagami imaging was presented to identify breast lesions, which was regarded as strain-compounding in the Nakagami domain to provide a new parameter associated with the scatterers and stiffness of tissues. Combining information from multiple Nakagami images obtained under different strain conditions can be useful to improve the ability to interpret the characteristics of breast tumors. Potential applications of these proposed imaging techniques include extending to the three-dimensional ultrasound for classifying different stages and grades of breast tumors, and assisting abdominal or musculoskeletal ultrasound for detecting characteristic symptoms and abnormalities. 葉秩光 2013 學位論文 ; thesis 125 en_US |
collection |
NDLTD |
language |
en_US |
format |
Others
|
sources |
NDLTD |
description |
博士 === 國立清華大學 === 生醫工程與環境科學系 === 101 === The primary goal of this work is to exploit the physical characteristics of feature type for better classifying breast tumors using ultrasound. The main topics can be divided to three certain parts. In chapter 2, ultrasound B-scan-based morphological and texture analysis and Nakagami parametric imaging were proposed to characterize breast tumors. These feature categories of ultrasound tissue characterization supplied information on different physical characteristics of breast tumors, by combining the above methods was expected to provide more clues for classifying breast tumors. The empirical results indicated that the combination of morphological-feature parameter (e.g., standard deviation of the shortest distance), texture feature (e.g., variance), and the Nakagami parameter resulted in the specificity and sensitivity both exceeded 88%, and the area under ROC curve of 0.95. In chapter 3, the feasibility of applying the elasticity imaging method to Nakagami imaging was investigated for visualizing the local redistributions of scatterers in a scattering medium with different stiffnesses. The preliminary results show the concept of the elasticity and scatterer characterizations being functionally complementary in classifying breast tumors. Consequently, the sequential Nakagami image frames obtained from different strain conditions would simply represent the relative tissue stiffness. In chapter 4, the use of a strain-compounding technique with Nakagami imaging was presented to identify breast lesions, which was regarded as strain-compounding in the Nakagami domain to provide a new parameter associated with the scatterers and stiffness of tissues. Combining information from multiple Nakagami images obtained under different strain conditions can be useful to improve the ability to interpret the characteristics of breast tumors. Potential applications of these proposed imaging techniques include extending to the three-dimensional ultrasound for classifying different stages and grades of breast tumors, and assisting abdominal or musculoskeletal ultrasound for detecting characteristic symptoms and abnormalities.
|
author2 |
葉秩光 |
author_facet |
葉秩光 廖尹吟 |
author |
廖尹吟 |
spellingShingle |
廖尹吟 Multi-parameters ultrasound imaging for breast cancer diagnosis |
author_sort |
廖尹吟 |
title |
Multi-parameters ultrasound imaging for breast cancer diagnosis |
title_short |
Multi-parameters ultrasound imaging for breast cancer diagnosis |
title_full |
Multi-parameters ultrasound imaging for breast cancer diagnosis |
title_fullStr |
Multi-parameters ultrasound imaging for breast cancer diagnosis |
title_full_unstemmed |
Multi-parameters ultrasound imaging for breast cancer diagnosis |
title_sort |
multi-parameters ultrasound imaging for breast cancer diagnosis |
publishDate |
2013 |
url |
http://ndltd.ncl.edu.tw/handle/13837860823591031692 |
work_keys_str_mv |
AT liàoyǐnyín multiparametersultrasoundimagingforbreastcancerdiagnosis AT liàoyǐnyín duōgōngnéngcānshùyúchāoyīnbōrǔáizhěnduàn |
_version_ |
1718073823164628992 |