Spatial statistics based feature descriptor for RF ultrasound data

T. Klein; M. Hansson; N. Navab

doi:10.1109/ISBI.2011.5872348

Spatial statistics based feature descriptor for RF ultrasound data

T. Klein, M. Hansson, N. Navab

Informatics 16 - Chair of Computer Aided Medical Procedures

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

In this paper we present a feature descriptor, based on a Markov random field (MRF) texture model, for radio-frequency (RF) ultrasound data. The proposed approach combines global data statistics in terms of a maximum-likelihood-estimated (MLE) distribution with local pattern characteristics employing MRF interaction parameters. This combining approach facilitates the encoding of the underlying nature of the ultrasound envelope data and therefore represents a powerful feature descriptor. Applicability and performance is showcased on RF data from a human neck.

Original language	English
Title of host publication	2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI'11
Pages	33-36
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2011.5872348
State	Published - 2011
Event	2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States Duration: 30 Mar 2011 → 2 Apr 2011

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Country/Territory	United States
City	Chicago, IL
Period	30/03/11 → 2/04/11

Keywords

Auto-model
Feature Descriptor
Markov Random Field
RF ultrasound
Ultrasound

Access to Document

10.1109/ISBI.2011.5872348

Cite this

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title = "Spatial statistics based feature descriptor for RF ultrasound data",

abstract = "In this paper we present a feature descriptor, based on a Markov random field (MRF) texture model, for radio-frequency (RF) ultrasound data. The proposed approach combines global data statistics in terms of a maximum-likelihood-estimated (MLE) distribution with local pattern characteristics employing MRF interaction parameters. This combining approach facilitates the encoding of the underlying nature of the ultrasound envelope data and therefore represents a powerful feature descriptor. Applicability and performance is showcased on RF data from a human neck.",

keywords = "Auto-model, Feature Descriptor, Markov Random Field, RF ultrasound, Ultrasound",

author = "T. Klein and M. Hansson and N. Navab",

year = "2011",

doi = "10.1109/ISBI.2011.5872348",

language = "English",

isbn = "9781424441280",

series = "Proceedings - International Symposium on Biomedical Imaging",

pages = "33--36",

booktitle = "2011 8th IEEE International Symposium on Biomedical Imaging",

note = "2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 ; Conference date: 30-03-2011 Through 02-04-2011",

}

Klein, T, Hansson, M & Navab, N 2011, Spatial statistics based feature descriptor for RF ultrasound data. in 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11., 5872348, Proceedings - International Symposium on Biomedical Imaging, pp. 33-36, 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11, Chicago, IL, United States, 30/03/11. https://doi.org/10.1109/ISBI.2011.5872348

Spatial statistics based feature descriptor for RF ultrasound data. / Klein, T.; Hansson, M.; Navab, N.
2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11. 2011. p. 33-36 5872348 (Proceedings - International Symposium on Biomedical Imaging).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - In this paper we present a feature descriptor, based on a Markov random field (MRF) texture model, for radio-frequency (RF) ultrasound data. The proposed approach combines global data statistics in terms of a maximum-likelihood-estimated (MLE) distribution with local pattern characteristics employing MRF interaction parameters. This combining approach facilitates the encoding of the underlying nature of the ultrasound envelope data and therefore represents a powerful feature descriptor. Applicability and performance is showcased on RF data from a human neck.

KW - Auto-model

KW - Feature Descriptor

KW - Markov Random Field

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T2 - 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11

Y2 - 30 March 2011 through 2 April 2011

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Spatial statistics based feature descriptor for RF ultrasound data

Abstract

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Keywords

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