Image Processing Reference
In-Depth Information
RF raw data
The authors want to thank the Department of Hemodynamic of Heart Institute to provide the
RF dada set. Beyond this, this work would not be possible without the help of Mariana, Paulo,
and John, great friends of my life, and the financial support of the Brazilian National Institute
of Science and Technology in Medicine Assisted by Scientific Computing (INCT—MAAC) and
National Council for Scientific and Technological Development (CNPq).
1 Introduction
Among the different modalities of medical images, ultrasound is arguably the most difficult
in which to perform segmentation. This is evident from a study of the first papers on segment-
ation, in which it was only possible to apply a threshold to the image in order to separate the
background from foreground due to the poor quality of the acquired data [ 1 ] .
At the same time, subsequent technological development has greatly increased the quality
of ultrasound images, especially in terms of signal-to-noise ratio and contrast-to-noise ratio
(CNR), resulting in improvements to image quality. Several studies have been highlighted that
aim to develop algorithms for the design of edges on objects contained in ultrasound images
[ 1 ] .
Ultrasonic tissue characterization (UTC) has become a well-established research field since
its first publication [ 2 ]. Thijssen [ 3 ] defines UTC as the assessment by ultrasound of quantitat-
ive information about the characteristics of biological tissue and their pathology. This quantit-
ative information is extracted from echographic data from radiofrequency (RF) data.
UTC applications abound in the literature and include classification of breast tissue [ 4 , 5 ],
liver [ 5 ] , heart [ 6 , 7 ] , eyes [ 8 ] , skin [ 9 ] , kidney [ 10 ] , and prostate [ 11 ].
Szabo [ 12 ] defines two general goals for UTC which can be applied to the above areas:(i)
(i) reveal the properties of tissues by analyzing the RF signal backscatered by ultrasound
transducer and
(ii) use information about the properties of the tissue to distinguish between the state of tissue
(healthy or diseased) or to detect changes in these properties when subjected to stimuli or
long periods of time in response to natural processes or medication.
Reaching these goals can be challenging since the interaction between biological tissue and
sound waves is extremely difficult to model and the process evolved in image segmentation
is strongly influenced by the quality of data and by the different parameters used during the
acquisition process of an image.
Parameters like contrast, brightness, and gain are adjusted by physicians to improve the
visualization of regions during the examination. These changes determine the digital imaging
and communications (DICOM) images that are recorded and the result cannot be changed
after the image has been acquired. This greatly complicates the comparison between patients
and the use of images in studies of groups of patients.
Thus, to avoid these complications and make image reconstructed independent of the para-
meters set by the physician, a reconstruction method from Intravascular ultrasound (IVUS)
images is proposed. This method is based on the RF signal stored by the equipment during
medical imaging examinations of IVUS.
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