title: Texture analysis of carotid artery atherosclerosis from three-dimensional ultrasound images.
abstract: OBJECTIVE
  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.
authors.affiliation: Imaging Research Laboratories, Robarts Research Institute, London, Ontario N6A 5K8, Canada. jawad@imaging.robarts.ca
authors.forename: Joseph
authors.initials: J
authors.lastname: Awad
authors.forename: Adam
authors.initials: A
authors.lastname: Krasinski
authors.forename: Grace
authors.initials: G
authors.lastname: Parraga
authors.forename: Aaron
authors.initials: A
authors.lastname: Fenster
mesh_terms: D000368:Aged; D000465:Algorithms; D050197:Atherosclerosis / Q000000981:diagnostic imaging* / Q000473:pathology; D000069059:Atorvastatin; D002339:Carotid Arteries / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D006538:Heptanoic Acids / Q000494:pharmacology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011758:Pyrroles / Q000494:pharmacology; D015203:Reproducibility of Results; D014463:Ultrasonography / Q000379:methods*
chemical_list: D006538:Heptanoic Acids; D011758:Pyrroles; D000069059:Atorvastatin