Ultrasound-based tissue characterization has been an active eld of cancer detection
in the past decades. The main concept behind various techniques is that the returning
ultrasound echoes carry tissue-dependent information that can be used to distinguish
tissue types. Recently, a new paradigm for tissue typing has been proposed which uses
ultrasound Radio Frequency (RF) echoes, recorded continuously from a xed location
of the tissue, to extract tissue-dependent information. This is hereafter referred to as
RF time series.
The source of tissue typing information in RF time series is not a well known
concept in the literature. However, there are two main hypotheses that describe the
informativeness of variations in RF time series. Such information could be partly due
to heat induction as a result of consistent eradiation of tissue with ultrasound beams
which results in a virtual displacement in RF echoes, and partly due to the acoustic
radiation force of ultrasound beams resulting in micro-vibration inside tissue.
In this thesis, we further investigate RF time series signals, collected at high
frequencies, by analyzing the properties of the RF displacements. It will be shown
that such displacements exhibit oscillatory behavior, emphasizing on the possible
micro-vibrations inside tissue, as well as linear incremental trend, indicating the e ect
of heat absorbtion of tissue.
i
The main focus of this thesis is to study the oscillatory behavior of RF displace-
ments in order to extract tissue-dependent features based on which tissue classi ca-
tion is performed. Using various linear and nonlinear tools, we study the properties
of such displacements in both frequency and time domain. Nonlinear analysis, based
on the theory of dynamical systems, is used to study the dynamical and geometrical
properties of RF displacements in the time domain.
Using Support Vector Machine (SVM), di erent tissue typing experiments are
performed to investigate the capability of the proposed features in tissue classi ca-
tion. It will be shown that the combination of such features can distinguish between
di erent tissue types almost perfectly. In addition, a feature reduction algorithm,
based on principle component analysis (PCA), is performed to reduce the number of
features required for a successful tissue classi cation. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2012-03-29 13:52:10.874
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OKQ.1974/7039 |
| Date | 29 March 2012 |
| Creators | NAJAFI YAZDI, MOHSEN |
| Contributors | Queen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.)) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner. |
| Relation | Canadian theses |
Page generated in 0.0018 seconds