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Texture analysis of carotid artery atherosclerosis from three‐dimensional ultrasound images
Author(s) -
Awad Joseph,
Krasinski Adam,
Parraga Grace,
Fenster Aaron
Publication year - 2010
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3301592
Subject(s) - wilcoxon signed rank test , artificial intelligence , image texture , common carotid artery , texture (cosmology) , medicine , pattern recognition (psychology) , ultrasound , mathematics , radiology , nuclear medicine , computer science , carotid arteries , image processing , mann–whitney u test , image (mathematics)
Purpose: To quantitatively evaluate local carotid arterial statin effects in 3D US images using multiclassifier image texture analysis tools. Methods: Texture analysis tools were used to evaluate the effect of 80 mg atorvastatin administered daily to patients with carotid stenosis compared to those treated with placebo. Using three‐dimensional carotid ultrasound images, 270 texture features from seven texture techniques were extracted from manually segmented carotid arteries based on the intima‐media boundary [vessel wall (VW)]. Individual texture features were compared to the previously determined changes in VW volume (VWV) using the distance between classes, the Wilcoxon rank sum test, and accuracy of the classifiers. Texture features that resulted in maximal classification accuracy from each texture technique were selected using Pudil's sequential floating forward selection (SFFS) as a method of ranking each technique. Finally, SFFS‐selected texture features from all texture techniques were used in combination with 24 classifier fusion techniques to improve classification accuracy. Results: Using the measurement of change in VWV, the distance between classes (DBC), Wilcoxon rank sum (WRS) p ‐value, and median accuracy measures (ACC) were 0.3798, 0.076, and 54.50%, respectively. Texture features improved the detection of statin‐related changes using DBC to 0.5199, using WRS to 0.002, and ACC to 63.87%, respectively. The texture techniques that most differentiated between atorvastatin and placebo classes were Fourier power spectrum and Laws texture energy measures. The average classification accuracy between atorvastatin and placebo classes was improved from 57.22 ± 12.11 % using VWV to 97.87 ± 3.93 % using specific texture features. Furthermore, the use of specific texture features resulted in the average area under the receiver‐operator characteristic curve (AUC) a value of 0.9988 ± 0.0069 compared to 0.617 ± 0.15 using carotid VWV. Conclusions: Based on DBC, WRS, ACC, and AUC texture features derived from 3D carotid ultrasound were observed to be more sensitive in detecting statin‐related changes in carotid atherosclerosis than VWV suggesting that texture classifiers can be used to detect changes in carotid atherosclerosis after therapy.