Speckle detection in ultrasonic images using unsupervised clustering techniques.

Arezou Akbarian Azar, Hasan Rivaz, Emad Boctor

Research output: Contribution to journalArticle

Abstract

In ultrasonic images, identification of speckled regions helps to estimate probe movement as well as improve performance of algorithms for adaptive speckle suppression and the elevational separation of B-scans by speckle decorrelation. By tracking FDS patch displacements over time we can calculate strain and detect tumor location. Previous studies for speckle detection were based on classification techniques which estimated parameters of the statistical distribution which were based on observation data and ultrasound echo envelope signal. However, in this study, we proposed a new combination of statistical features which were extracted from the ultrasound images and explored their properties for the speckle detection. These features were used as inputs to the unsupervised clustering algorithms for the speckle classification. We used five different types of unsupervised techniques and compared their performance by feeding different combinations of the statistical features. In order to quantitatively compare statistical features and classification methods, as ground truth, we used simulations of cyst and fetus ultrasound images which were generated using Field II ultrasound simulation program[1]. Initial results showed that by combining two statistical models (K and Rayleigh distributions) we can get best speck detection signatures to feed unsupervised classifiers and maximize speckle detection performance.

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Speckle
Ultrasonics
Cluster Analysis
Statistical Distributions
Statistical Models
Cysts
Fetus
Observation
Clustering algorithms
Tumors
Neoplasms
Classifiers

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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title = "Speckle detection in ultrasonic images using unsupervised clustering techniques.",
abstract = "In ultrasonic images, identification of speckled regions helps to estimate probe movement as well as improve performance of algorithms for adaptive speckle suppression and the elevational separation of B-scans by speckle decorrelation. By tracking FDS patch displacements over time we can calculate strain and detect tumor location. Previous studies for speckle detection were based on classification techniques which estimated parameters of the statistical distribution which were based on observation data and ultrasound echo envelope signal. However, in this study, we proposed a new combination of statistical features which were extracted from the ultrasound images and explored their properties for the speckle detection. These features were used as inputs to the unsupervised clustering algorithms for the speckle classification. We used five different types of unsupervised techniques and compared their performance by feeding different combinations of the statistical features. In order to quantitatively compare statistical features and classification methods, as ground truth, we used simulations of cyst and fetus ultrasound images which were generated using Field II ultrasound simulation program[1]. Initial results showed that by combining two statistical models (K and Rayleigh distributions) we can get best speck detection signatures to feed unsupervised classifiers and maximize speckle detection performance.",
author = "Azar, {Arezou Akbarian} and Hasan Rivaz and Emad Boctor",
year = "2011",
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N2 - In ultrasonic images, identification of speckled regions helps to estimate probe movement as well as improve performance of algorithms for adaptive speckle suppression and the elevational separation of B-scans by speckle decorrelation. By tracking FDS patch displacements over time we can calculate strain and detect tumor location. Previous studies for speckle detection were based on classification techniques which estimated parameters of the statistical distribution which were based on observation data and ultrasound echo envelope signal. However, in this study, we proposed a new combination of statistical features which were extracted from the ultrasound images and explored their properties for the speckle detection. These features were used as inputs to the unsupervised clustering algorithms for the speckle classification. We used five different types of unsupervised techniques and compared their performance by feeding different combinations of the statistical features. In order to quantitatively compare statistical features and classification methods, as ground truth, we used simulations of cyst and fetus ultrasound images which were generated using Field II ultrasound simulation program[1]. Initial results showed that by combining two statistical models (K and Rayleigh distributions) we can get best speck detection signatures to feed unsupervised classifiers and maximize speckle detection performance.

AB - In ultrasonic images, identification of speckled regions helps to estimate probe movement as well as improve performance of algorithms for adaptive speckle suppression and the elevational separation of B-scans by speckle decorrelation. By tracking FDS patch displacements over time we can calculate strain and detect tumor location. Previous studies for speckle detection were based on classification techniques which estimated parameters of the statistical distribution which were based on observation data and ultrasound echo envelope signal. However, in this study, we proposed a new combination of statistical features which were extracted from the ultrasound images and explored their properties for the speckle detection. These features were used as inputs to the unsupervised clustering algorithms for the speckle classification. We used five different types of unsupervised techniques and compared their performance by feeding different combinations of the statistical features. In order to quantitatively compare statistical features and classification methods, as ground truth, we used simulations of cyst and fetus ultrasound images which were generated using Field II ultrasound simulation program[1]. Initial results showed that by combining two statistical models (K and Rayleigh distributions) we can get best speck detection signatures to feed unsupervised classifiers and maximize speckle detection performance.

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