De-speckle filtering in ultrasound imaging of the carotid artery

C. I. Christodoulou; C. Loizou; C. S. Pattichis; M. Pantziaris; E. Kyriakou; M. S. Pattichis; C. N. Schizas; A. Nicolaides

De-speckle filtering in ultrasound imaging of the carotid artery

C. I. Christodoulou, C. Loizou, C. S. Pattichis, M. Pantziaris, E. Kyriakou, M. S. Pattichis, C. N. Schizas, A. Nicolaides

Department of Computer Science

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

Abstract

The main objective of this paper is to evaluate the classification performance of de-speckle filtering on ultrasound imaging of the carotid atherosclerotic plaque. The following procedure was investigated on 230 images (recorded from 115 symptomatic, and 115 asymptomatic subjects): (i) six different de-speckle filters were used based on first order and higher order local statistics, anisotropic diffusion, and geometric properties; (ii) nine different texture feature sets were extracted, and (iii) the k-nearest neighbor classifier was used to classify a plaque as symptomatic or asymptomatic. The de-speckle filters based on higher order statistics, anisotropic speckle diffusion, and geometric properties gave a slightly higher percentage of correct classifications score than the original images.

Original language	English
Pages (from-to)	1027-1028
Number of pages	2
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	2
Publication status	Published - 2002

Keywords

Carotid plaque
De-speckle filtering
Texture analysis

Cite this

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keywords = "Carotid plaque, De-speckle filtering, Texture analysis",

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AU - Christodoulou, C. I.

AU - Loizou, C.

AU - Pattichis, C. S.

AU - Pantziaris, M.

AU - Kyriakou, E.

AU - Pattichis, M. S.

AU - Schizas, C. N.

AU - Nicolaides, A.

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N2 - The main objective of this paper is to evaluate the classification performance of de-speckle filtering on ultrasound imaging of the carotid atherosclerotic plaque. The following procedure was investigated on 230 images (recorded from 115 symptomatic, and 115 asymptomatic subjects): (i) six different de-speckle filters were used based on first order and higher order local statistics, anisotropic diffusion, and geometric properties; (ii) nine different texture feature sets were extracted, and (iii) the k-nearest neighbor classifier was used to classify a plaque as symptomatic or asymptomatic. The de-speckle filters based on higher order statistics, anisotropic speckle diffusion, and geometric properties gave a slightly higher percentage of correct classifications score than the original images.

AB - The main objective of this paper is to evaluate the classification performance of de-speckle filtering on ultrasound imaging of the carotid atherosclerotic plaque. The following procedure was investigated on 230 images (recorded from 115 symptomatic, and 115 asymptomatic subjects): (i) six different de-speckle filters were used based on first order and higher order local statistics, anisotropic diffusion, and geometric properties; (ii) nine different texture feature sets were extracted, and (iii) the k-nearest neighbor classifier was used to classify a plaque as symptomatic or asymptomatic. The de-speckle filters based on higher order statistics, anisotropic speckle diffusion, and geometric properties gave a slightly higher percentage of correct classifications score than the original images.

KW - Carotid plaque

KW - De-speckle filtering

KW - Texture analysis

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JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

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De-speckle filtering in ultrasound imaging of the carotid artery

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Keywords

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