title: Sonographic feature extraction of cervical lymph nodes and its relationship with segmentation methods.
abstract: OBJECTIVE
  The purpose of this study was to extract quantitative features for characterization of cervical lymph nodes on sonographic images and analyze the effect of a semiautomated segmentation method on the feature extraction.
  
  
  METHODS
  Contours of 186 cervical lymph nodes on sonographic images were separately delineated by 2 radiologists (R1 and R2) and a semiautomated segmentation method. For each node, 10 kinds of sonographic features (including 3 parameters of size; 12 parameters of margin; 4 parameters of nodal border; 10 parameters of echogeneity; and 1 parameter of shape, echogenicity, medulla ratio, medulla distribution, vascular density, and vascular pattern, respectively) were quantified by a computerized scheme based on the segmented contour. Correlations between the quantitative parameter and the radiologists' consensus grading were computed to assess the effectiveness of these parameters. Concerning the 14 best correlated parameters, the effect of the segmentation stage on the feature extraction was estimated by comparing the parameter values calculated under different segmentations in terms of relative ultimate measurement accuracy.
  
  
  RESULTS
  Good correlations between the computerized scheme and radiologists were seen in features of size, nodal border, shape, echogenicity, medulla ratio, medulla distribution, vascular density, and vascular pattern, whereas 10 of 12 parameters of margin features and 8 of 10 parameters of echogeneity features showed poor correlations. Paired t tests comparing the relative ultimate measurement accuracy computed using the R1-R2 and the R1-computer pairing showed no significant difference on 11 parameters for the 14 parameters analyzed.
  
  
  CONCLUSIONS
  The computerized feature parameters may be used as assisted indices for evaluating cervical lymphadenopathies from sonographic images. The semiautomated segmentation method satisfied the accuracy requirement of the feature extraction.
authors.affiliation: Department of Electronic Engineering, Fudan University, Shanghai 200433, China.
authors.forename: Junhua
authors.initials: J
authors.lastname: Zhang
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Wang
authors.forename: Yi
authors.initials: Y
authors.lastname: Dong
authors.forename: Yi
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008198:Lymph Nodes / Q000098:blood supply / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D009333:Neck / Q000000981:diagnostic imaging; D010363:Pattern Recognition, Automated; D018608:Ultrasonography, Doppler