title: Classification of scattering media within benign and malignant breast tumors based on ultrasound texture-feature-based and Nakagami-parameter images.
abstract: OBJECTIVE
  Benign and malignant tumors can be classified by using texture analysis of the ultrasound B-scan image to describe the variation in the echogenicity of scatterers. The recently proposed ultrasonic Nakagami parametric image has also been used to detect the concentrations and arrangements of scatterers for tumor characterization applications. B-scan-based texture analysis and the Nakagami parametric image are functionally complementary in ultrasonic tissue characterizations and this study aimed to combine these methods in order to improve the ability to characterize breast tumors.
  
  
  METHODS
  To validate this concept, radio-frequency data obtained from 130 clinical cases were used to construct the texture-feature parametric image and the Nakagami parametric image. Four texture-feature parameters based on a gray-level co-occurrence matrix (homogeneity, contrast, energy, and variance) and the Nakagami parameters of the benign and malignant tumors were calculated. The usefulness of an individual parameter was determined and scatter graphs indicated the relationship between two selected texture-feature parameters. Fisher's linear discriminant analysis was used to combine the selected texture-feature parameters with the Nakagami parameter. The performance in classifying tumors was evaluated based on the receiver operating characteristic curve.
  
  
  RESULTS
  The results indicated that there is a trade-off between sensitivity and specificity when using an individual texture-feature parameter or when combining two such correlated parameters to discriminate benign and malignant cases. However, the best performance was obtained when combining selected texture-feature parameters with the Nakagami parameter.
  
  
  CONCLUSIONS
  The study findings suggest that combining B-scan-based texture analysis and the Nakagami parametric image could improve the ability to classify benign and malignant breast tumors.
authors.affiliation: Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 30013, Taiwan.
authors.forename: Yin-Yin
authors.initials: YY
authors.lastname: Liao
authors.forename: Po-Hsiang
authors.initials: PH
authors.lastname: Tsui
authors.forename: Chia-Hui
authors.initials: CH
authors.lastname: Li
authors.forename: King-Jen
authors.initials: KJ
authors.lastname: Chang
authors.forename: Wen-Hung
authors.initials: WH
authors.lastname: Kuo
authors.forename: Chien-Cheng
authors.initials: CC
authors.lastname: Chang
authors.forename: Chih-Kuang
authors.initials: CK
authors.lastname: Yeh
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D018270:Carcinoma, Ductal, Breast / Q000000981:diagnostic imaging / Q000473:pathology; D018226:Fibroadenoma / Q000000981:diagnostic imaging / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D014463:Ultrasonography