Prof. Oliver M. Jeromin Profile

Prof. Oliver M. Jeromin

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Proceedings Article | 10 March 2006 Paper

Variogram methods for texture classification of atherosclerotic plaque ultrasound images

Oliver Jeromin, Marios Pattichis, Constantinos Pattichis, Efthyvoulos Kyriacou, Andrew Nicolaides

Proceedings Volume 6144, 61440D (2006) https://doi.org/10.1117/12.652355

KEYWORDS: Ultrasonography, Image segmentation, Data modeling, Arteries, Image classification, Statistical analysis, Image analysis, Tissues, Image processing, Distance measurement

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Stroke is the third leading cause of death in the western world and the major cause of disability in adults. The type and stenosis of extracranial carotid artery disease is often responsible for ischemic strokes, transient ischemic attacks (TIAs) or amaurosis fugax (AF). The identification and grading of stenosis can be done using gray scale ultrasound scans. The appearance of B-scan pictures containing various granular structures makes the use of texture analysis techniques suitable for computer assisted tissue characterization purposes. The objective of this study is to investigate the usefulness of variogram analysis in the assessment of ultrasound plague morphology. The variogram estimates the variance of random fields, from arbitrary samples in space. We explore stationary random field models based on the variogram, which can be applied in ultrasound plaque imaging leading to a Computer Aided Diagnosis (CAD) system for the early detection of symptomatic atherosclerotic plaques. Non-parametric tests on the variogram coefficients show that the cofficients coming from symptomatic versus asymptomatic plaques come from distinct distributions. Furthermore, we show significant improvement in class separation, when a log point-transformation is applied to the images, prior to variogram estimation. Model fitting using least squares is explored for anisotropic variograms along specific directions. Comparative classification results, show that variogram coefficients can be used for the early detection of symptomatic cases, and also exhibit the largest class distances between symptomatic and asymptomatic plaque images, as compared to over 60 other texture features, used in the literature.

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