title: Quantitatively characterizing the textural features of sonographic images for breast cancer with histopathologic correlation.
abstract: OBJECTIVE
  In this study, quantitative characterization of sonographic image texture and its correlation with histopathologic findings was developed for facilitating clinical diagnosis. A statistical feature matrix was applied to quantify the texture difference (ie, the dissimilarity) of the sonographic images for malignant and benign breast tumors.
  
  
  METHODS
  Thirty-three patients were recruited for this study. Imaging was performed on a commercially available sonographic imaging system in clinical use. The parameters used for image acquisition were kept the same during clinical examination.
  
  
  RESULTS
  On the basis of dissimilarity values, 3 phenomena were noted in the relatively large malignancies studied. First, stellate carcinoma showed the least dissimilarity on sonographic images; second, circumscribed carcinoma showed the most dissimilarity; and third, malignant tissue mixed with fibrous and cellular parts (dense lymphocyte infiltration and prominent intraductal tumors) had dissimilarity values in between. Image textures with smaller dissimilarity values (especially for those values <4.4 in our study) are likely to be stellate carcinoma.
  
  
  CONCLUSIONS
  From the experimental results, it is shown that the cellular and fibrous content with spatial distribution of breast masses determine the dissimilarity values on sonographic images. The dissimilarity may be used to quantitatively represent the image texture and is well correlated with the histopathologic description.
authors.affiliation: Institute of Biomedical Engineering, National Cheng Kung University, 1 Ta-Hsueh Rd, Tainan 701, Taiwan. chensj@mail.ncku.edu.tw
authors.forename: Shao-Jer
authors.initials: SJ
authors.lastname: Chen
authors.forename: Kuo-Sheng
authors.initials: KS
authors.lastname: Cheng
authors.forename: Yuan-Chang
authors.initials: YC
authors.lastname: Dai
authors.forename: Yung-Nien
authors.initials: YN
authors.lastname: Sun
authors.forename: Yen-Ting
authors.initials: YT
authors.lastname: Chen
authors.forename: Ku-Yaw
authors.initials: KY
authors.lastname: Chang
authors.forename: Sung-Nien
authors.initials: SN
authors.lastname: Yu
authors.forename: Tsai-Wang
authors.initials: TW
authors.lastname: Chang
authors.forename: Hong-Ming
authors.initials: HM
authors.lastname: Tsai
authors.forename: Chin-Chiang
authors.initials: CC
authors.lastname: Hsien
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D044963:Biopsy, Fine-Needle; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology* / Q000601:surgery; D018270:Carcinoma, Ductal, Breast / Q000000981:diagnostic imaging / Q000473:pathology / Q000601:surgery; D002285:Carcinoma, Intraductal, Noninfiltrating / Q000000981:diagnostic imaging / Q000473:pathology / Q000601:surgery; D016001:Confidence Intervals; D003937:Diagnosis, Differential; D005260:Female; D018226:Fibroadenoma / Q000000981:diagnostic imaging / Q000473:pathology / Q000601:surgery; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008408:Mastectomy / Q000379:methods; D008875:Middle Aged; D003557:Phyllodes Tumor / Q000000981:diagnostic imaging / Q000473:pathology / Q000601:surgery; D016217:Ultrasonography, Mammary