ANALYSIS OF ULTRASOUND IMAGE TEXTURE VIA GENERALIZED RICIAN STATISTICS.

Michael F. Insana; Robert F. Wagner; Brian S. Garra; David G. Brown; Thomas H. Shawker

ANALYSIS OF ULTRASOUND IMAGE TEXTURE VIA GENERALIZED RICIAN STATISTICS.

Michael F. Insana, Robert F. Wagner, Brian S. Garra, David G. Brown, Thomas H. Shawker

Research output: Contribution to journal › Article › peer-review

Abstract

Tissue signatures are obtained from the first- and second-order statistics of ultrasonic B-scan texture. Laboratory measurements and early clinical results show that the image may be segmented to discriminate between different normal tissues and to detect abnormal conditions based on a multidimensional feature space. These features describe the intrinsic backscatter properties of the tissues imaged and may be used as the basis of an automatic tissue characterization algorithm.

Original language	English (US)
Pages (from-to)	743-748
Number of pages	6
Journal	Optical Engineering
Volume	25
Issue number	6
State	Published - Jun 1 1986
Externally published	Yes

ASJC Scopus subject areas

Atomic and Molecular Physics, and Optics
Engineering(all)

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abstract = "Tissue signatures are obtained from the first- and second-order statistics of ultrasonic B-scan texture. Laboratory measurements and early clinical results show that the image may be segmented to discriminate between different normal tissues and to detect abnormal conditions based on a multidimensional feature space. These features describe the intrinsic backscatter properties of the tissues imaged and may be used as the basis of an automatic tissue characterization algorithm.",

author = "Insana, {Michael F.} and Wagner, {Robert F.} and Garra, {Brian S.} and Brown, {David G.} and Shawker, {Thomas H.}",

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AU - Wagner, Robert F.

AU - Garra, Brian S.

AU - Brown, David G.

AU - Shawker, Thomas H.

PY - 1986/6/1

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AB - Tissue signatures are obtained from the first- and second-order statistics of ultrasonic B-scan texture. Laboratory measurements and early clinical results show that the image may be segmented to discriminate between different normal tissues and to detect abnormal conditions based on a multidimensional feature space. These features describe the intrinsic backscatter properties of the tissues imaged and may be used as the basis of an automatic tissue characterization algorithm.

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ANALYSIS OF ULTRASOUND IMAGE TEXTURE VIA GENERALIZED RICIAN STATISTICS.

Abstract

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