04-05-2012, 03:04 PM
Ultrasound Imaging and Image Segmentation in the area of
Ultrasound
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Abstract
Segmentation remains a necessary step in medical imaging to obtain qualitative
measurements such as the location of objects of interest as well as for quantitative
measurements such as area, volume or the analysis of dynamic behavior of anatomical
structures over time. Among these images, ultrasound images play a crucial role, because
they can be produced at video-rate and therefore allow a dynamic analysis of moving
structures. Moreover, the acquisition of these images is non-invasive, cheap, and does not
require ionizing radiations compared to other medical imaging techniques. On the other
hand, the automatic segmentation of anatomical structures in ultrasound imagery is a real
challenge due to acoustic interferences (speckle noise) and artifacts which are inherent in
these images. Here we discuss the ultrasound image segmentation methods, in a broad sense,
focusing on techniques developed for medical. First, we present basic methods of image
segmentation and forming of ultrasound images. After that we discuss the basics on
ultrasound image segmentation. Second section explains the ultrasonic image segmentation
methods based on clinical applications. On the other hand Third section explains the
ultrasound image segmentation based on particular methodology.
Keywords: Segmentation; Ultrasound; Speckle Noise; Artifacts; ionizing radiations
Introduction
Image analysis usually refers to processing of images by computer with the goal of
finding what objects are presented in the image. Image segmentation is one of the most
critical tasks in automatic image analysis.
In a standard ultrasound system there are three basic types of data available for analysis:
radiofrequency (RF) signals, envelope-detected signals, and B-mode images. A
transmit/receive ultrasound transducer receives multiple analogue radio-frequency (RF)
signals which are converted to digital RF signals and beam formed into a single RF signal.
The RF signal is then filtered, and envelope detection is performed to give an envelopedetected
signal. Finally, the envelope-detected signal undergoes log compression, and often
proprietary post-processing is applied to give a grayscale representation. The resulting signals
are then interpolated and rasterized to give a B-mode or display image [1].
Different Methods of Image Segmentation
Segmentation Algorithms mainly based on two basic properties:
1. Discontinuity (Edge based Approaches) : Based on abrupt change in Intensity
International Journal of Advanced Science and Technology
Vol. 24, November, 2010
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2. Similarity(Region based Approaches ) : Similar according to predefined Criterion
Edge Based Approaches
Edge detection is a well-developed field on its own within image processing. Region
boundaries and edges are closely related, since there is often a sharp adjustment in intensity at
the region boundaries. Edge detection techniques have therefore been used as the base of
another segmentation technique.
Similarity
Thresholding
Segmentation problems requiring multiple thresholds are best solved using region growing
methods. Thresholding can be viewed as: