Segmentation Methods in Ultrasound Images

Segmentation Methods in Ultrasound Images

Farhang Sahba (Medical Imaging Analyst, Canada)
DOI: 10.4018/978-1-60566-314-2.ch030
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Abstract

Ultrasound imaging now has widespread clinical use. It involves exposing a part of the body to highfrequency sound waves in order to generate images of the inside of the body. Because it is a real-time procedure, the ultrasound images show the movement of the body’s internal structure as well. It is usually a painless medical test and its procedures seem to be safe. Despite recent improvement in the quality of information from an ultrasound device, these images are still a challenging case for segmentation. Thus, there is much interest in understanding how to apply an image segmentation task to ultrasound data and any improvements in this regard are desirable. Many methods have been introduced in existing literature to facilitate more accurate automatic or semi-automatic segmentation of ultrasound images. This chapter is a basic review of the works on ultrasound image segmentation classified by application areas, including segmentation of prostate transrectal ultrasound (TRUS), breast ultrasound, and intravascular ultrasound (IVUS) images.
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Introduction

Among different image modalities, ultrasound imaging is one of the most widely used technologies for the diagnosis and treatment of diseases such as breast and prostate cancer. Ultrasound equipment is less expensive to purchase and maintain than many other imaging systems such as X-ray, computed tomography (CT), or magnetic resonance imaging (MRI). These images are the result of reflection, refraction, and deflection of ultrasound beams from different types of tissue with different acoustic impedances. The detection of the object boundaries in such images is crucial for diagnostic and classification purposes.

However, attenuation, speckle, shadows, and signal dropout can result in missing or diffused boundaries. Also the contrast between areas of interest is often low. These obstacles make segmentation of these images a challenge. Further complications arise when the quality of the image is influenced by the type and particular settings of the machine. Despite these factors, ultrasound imaging still remains an important tool for clinical applications and any effort to improve segmentation of these images is highly desirable. Thus, there is currently an interest in understanding how to apply image segmentation to ultrasound data. Figure 1 demonstrates the basic principle of an ultrasound imaging transducer. Using an ultrasound transducer, a pulse of energy is transmitted into the body along the path shown by line 1. After this beam encounters any surface, including tissue or structures within an organ, a part of the transmitted energy is backscattered along the original trajectory and received by the transducer which now acts as a receiver. These returning waves are converted to electrical signals, amplified, and finally shown. After that, the direction of the transmitted beam changes to attain the data from the next line close to the first one. The ultrasound transducer repeats the same procedure to cover 64-256 lines and makes the entire image (Webb, 2003).

Figure 1.

The basic principle of an ultrasound imaging transducer (© 2003 IEEE,   Reprinted, with permission from IEEE Press Series in Biomedical Engineering 2003. “Introduction to Biomedical Imaging”, by A. Webb).

This chapter contains an overview of the ideas representing the ultrasound segmentation problem in particular clinical applications.

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Ultrasound Image Segmentation According To Clinical Applications

Based on clinical application, ultrasound image segmentation can be categorized in various groups. In this section, we mention some important methods in each group.

Key Terms in this Chapter

Image Segmentation: This is an important image processing task that partitions the image into meaningful regions.

Prostate Ultrasound: Generation of ultrasound images from the prostate for diagnostic and treatment purposes.

Ultrasound Imaging: This is an ultrasound-based diagnostic imaging technique used for visualizing internal organs and their structures and possible lesions.

Breast Ultrasound: Generation of ultrasound images from the breast for diagnostic and treatment purposes.

Intravascular Ultrasound: This is an ultrasound medical imaging methodology that uses a catheter with a miniaturized ultrasound probe to visualize the inside walls of blood vessels.

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Table of Contents
Preface
Themis P. Exarchos, Athanasios Papadopoulos, Dimitrios I. Fotiadis
Chapter 1
Ioannis Dimou, Michalis Zervakis, David Lowe, Manolis Tsiknakis
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Chapter 2
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Though an unparalleled amount and diversity of imaging and clinical data are now collected as part of routine care, this information is not... Sample PDF
Integrating Imaging and Clinical Data for Decision Support
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Chapter 3
Spyretta Golemati, John Stoitsis, Konstantina S. Nikita
The estimation of motion of the myocardial and arterial wall is important for the quantification of tissue elasticity and contractility and has... Sample PDF
Analysis and Quantification of Motion within the Cardiovascular System: Implications for the Mechanical Strain of Cardiovascular Structures
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Chapter 4
Christos V. Bourantas, Katerina Naka, Dimitrios Fotiadis, Lampros Michalis
Intracoronary Ultrasound (ICUS) imaging is an intravascular catheter-based technique which provides real-time, high resolution, cross-sectional... Sample PDF
New Developments in Intracoronary Ultrasound Processing
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Chapter 5
Stavroula Mougiakakou, Ioannis Valavanis, Alexandra Nikita, Konstantina S. Nikita
Recent advances in computer science provide the intelligent computation tools needed to design and develop Diagnostic Support Systems (DSSs) that... Sample PDF
Diagnostic Support Systems and Computational Intelligence: Differential Diagnosis of Hepatic Lesions from Computed Tomography Images
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Chapter 6
Marotesa Voultsidou, J. Michael Herrmann
Indicative features of an fMRI data set can be evaluated by methods provided by theory of random matrices (RMT). RMT considers ensembles of matrices... Sample PDF
Significance Estimation in fMRI from Random Matrices
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Chapter 7
Dimitrios C. Karampinos, Robert Dawe, Konstantinos Arfanakis, John G. Georgiadis
Diffusion Magnetic Resonance Imaging (diffusion MRI) can provide important information about tissue microstructure by probing the diffusion of water... Sample PDF
Optimal Diffusion Encoding Strategies for Fiber Mapping in Diffusion MRI
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Chapter 8
Dimitrios G. Tsalikakis, Petros S. Karvelis, Dimitrios I. Fotiadis
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Chapter 9
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Chapter 10
Lena Costaridou, Spyros Skiadopoulos, Anna Karahaliou, Nikolaos Arikidis, George Panayiotakis
Breast cancer is the most common cancer in women worldwide. Mammography is currently the most effective modality in detecting breast cancer... Sample PDF
Computer-Aided Diagnosis in Breast Imaging: Trends and Challenges
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Chapter 11
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Chapter 12
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Chapter 13
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Chapter 14
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Chapter 15
Evanthia E. Tripoliti, Dimitrios I. Fotiadis, Konstantia Veliou
Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging (MRI) modality which can significantly improve our understanding of the brain... Sample PDF
Diffusion Tensor Imaging and Fiber Tractography
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Chapter 16
Anastasios Koutlas, Dimitrios I. Fotiadis
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Chapter 17
Arcangelo Merla
This chapter presents an overview on recent developments in the field of clinical applications of the functional infrared imaging. The functional... Sample PDF
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Chapter 18
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Chapter 19
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Chapter 20
Petros S. Karvelis, Dimitrios I. Fotiadis
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Chapter 21
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The segmentation of microscopic images is a challenging application that can have numerous applications ranging from prognosis to diagnosis.... Sample PDF
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Chapter 22
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Chapter 23
C. Papaodysseus, P. Rousopoulos, D. Arabadjis, M. Panagopoulos, P. Loumou
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Chapter 24
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Chapter 25
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Chapter 26
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Chapter 27
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Chapter 28
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Chapter 29
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Chapter 30
Farhang Sahba
Ultrasound imaging now has widespread clinical use. It involves exposing a part of the body to highfrequency sound waves in order to generate images... Sample PDF
Segmentation Methods in Ultrasound Images
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