Quantitatively characterizing the textural features of sonographic images for breast cancer with histopathologic correlation

Shao Jer Chen, Kuo Sheng Cheng, Yuan Chang Dai, Yung Nien Sun, Yen Ting Chen, Ku Yaw Chang, Sung Nien Yu, Tsai Wang Chang, Hong Ming Tsai, Chin Chiang Hsien

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)651-661
Number of pages11
JournalJournal of Ultrasound in Medicine
Volume24
Issue number5
DOIs
Publication statusPublished - 2005 May

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

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