3-D ultrasound texture classification using run difference matrix

Wei Ming Chen, Ruey Feng Chang, Shou Jen Kuo, Cheng Shyong Chang, Kyung Moon Woo, Shou Tung Chen, Dar Ren Chen

Research output: Contribution to journalArticle

21 Scopus citations

Abstract

Ultrasonography is one of the most useful diagnostic tools for human soft tissue and it is in routine use in nearly all hospitals and many physicians' offices and clinics. However, the diagnosis mostly depends upon the personal experiences of the physicians. Moreover, the surface features and internal architecture of a tumor are not easy to be demonstrated simultaneously using the conventional two-dimensional (2-D) ultrasound. Recently, three-dimensional (3-D) ultrasound has been developed and allows the physician to view the 3-D anatomy. 3-D breast US can provide transverse, longitudinal planes as well as in addition simultaneously the coronal plane. This additional information has been proved to be helpful for clinical applications. In this paper, a new approach of texture classification of 3-D ultrasound breast diagnosis using run difference matrix with neural networks is developed. The test 3-D US image database includes 54 malignant and 161 benign tumors. In the experiments, the area index Az under the ROC curve of the proposal 3-D RDM method can achieve 0.9680. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the proposed 3-D RDM method is 91.9%(148/161), 88.9%(48/54), 93.5%(100/107), 87.3%(48/55), and 94.3%(100/105), respectively.

Original languageEnglish
Pages (from-to)763-770
Number of pages8
JournalUltrasound in Medicine and Biology
Volume31
Issue number6
DOIs
StatePublished - 1 Jun 2005

Keywords

  • 3-D breast ultrasound
  • Breast tumor
  • Neural network
  • Run difference matrix

Cite this

Chen, W. M., Chang, R. F., Kuo, S. J., Chang, C. S., Woo, K. M., Chen, S. T., & Chen, D. R. (2005). 3-D ultrasound texture classification using run difference matrix. Ultrasound in Medicine and Biology, 31(6), 763-770. https://doi.org/10.1016/j.ultrasmedbio.2005.01.014