title: Influence of the measurement method of features in ultrasound images of the thyroid in the diagnosis of Hashimoto's disease.
abstract: BACKGROUND
  This paper shows the influence of a measurement method of features in the diagnosis of Hashimoto's disease. Sensitivity of the algorithm to changes in the parameters of the ROI, namely shift, resizing and rotation, has been presented. The obtained results were also compared to the methods known from the literature in which decision trees or average gray level thresholding are used.
  
  
  METHODS
  In the study, 288 images obtained from patients with Hashimoto's disease and 236 images from healthy subjects have been analyzed. For each person, an ultrasound examination of the left and right thyroid lobe in transverse and longitudinal sections has been performed.
  
  
  METHODS
  With the use of the developed algorithm, a discriminant analysis has been conducted for the following five options: linear, diaglinear, quadratic, diagquadratic and mahalanobis. The left and right thyroid lobes have been analyzed both together and separately in transverse and longitudinal sections. In addition, the algorithm enabled to analyze specificity and sensitivity as well as the impact of sensitivity of ROI shift, repositioning and rotation on the measured features.
  
  
  RESULTS
  The analysis has shown that the highest accuracy was obtained for the longitudinal section (LD) with the method of linear, yielding sensitivity = 76%, specificity = 95% and accuracy ACC = 84%. The conducted sensitivity assessment confirms that changes in the position and size of the ROI have little effect on sensitivity and specificity. The analysis of all cases, that is, images of the left and right thyroid lobes in transverse and longitudinal sections, has shown specificity ranging from 60% to 95% and sensitivity from 62% to 89%. Additionally, it was shown that the value of ACC for the method using decision trees as a classifier is equal to 84% for the analyzed data. Thresholding of average brightness of the ROI gave ACC equal to 76%.
authors.affiliation: Department of Computer Biomedical Systems, University of Silesia, Institute of Computer Science, Sosnowiec, Poland. robert.koprowski@us.edu.pl
authors.forename: Robert
authors.initials: R
authors.lastname: Koprowski
authors.forename: Anna
authors.initials: A
authors.lastname: Korzyńska
authors.forename: Zygmunt
authors.initials: Z
authors.lastname: Wróbel
authors.forename: Witold
authors.initials: W
authors.lastname: Zieleźnik
authors.forename: Agnieszka
authors.initials: A
authors.lastname: Witkowska
authors.forename: Justyna
authors.initials: J
authors.lastname: Małyszek
authors.forename: Waldemar
authors.initials: W
authors.lastname: Wójcik
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D016022:Case-Control Studies; D016002:Discriminant Analysis; D050031:Hashimoto Disease / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D013961:Thyroid Gland / Q000000981:diagnostic imaging*; D014463:Ultrasonography; D055815:Young Adult