Intensity-invariant texture analysis for classification of bi-rads category 3 breast masses

Chung Ming Lo, Woo Kyung Moon, Chiun Sheng Huang, Jeon Hor Chen, Min Chun Yang, Ruey Feng Chang

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

Radiologists likely incorrectly classify benign masses as Breast Imaging Reporting and Data System (BIRADS) category 3. A computer-aided diagnosis (CAD) system was developed in this study as a second viewer to avoid misclassification of carcinomas. Sixty-nine biopsy-proven BI-RADS category 3 masses, including 21 malignant and 48 benign masses, were used to evaluate the CAD system. To improve the texture features, gray-scale variations between images were reduced by transforming pixels into intensity-invariant ranklet coefficients. The textures of the tumor and speckle pixels were extracted from the transformed ranklet images to provide more robust features than in conventionalCADsystems. As a result, tumor texture and speckle texture with ranklet transformation achieved significantly better areas under the receiver operating characteristic curve (Az) compared with those without ranklet transformation (Az = 0.83 vs. 0.58 and Az = 0.80 vs. 0.56, p value <0.05). The improved CAD system can be a second reader to confirm the classification of BI-RADS category 3 masses.

Original languageEnglish
Pages (from-to)2039-2048
Number of pages10
JournalUltrasound in Medicine and Biology
Volume41
Issue number7
DOIs
Publication statusPublished - 2015
Externally publishedYes

Keywords

  • Breast cancer
  • Breast imaging and reporting data system
  • Computer-aided diagnosis
  • Ranklet
  • Ultrasound

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology
  • Biophysics

Fingerprint Dive into the research topics of 'Intensity-invariant texture analysis for classification of bi-rads category 3 breast masses'. Together they form a unique fingerprint.

  • Cite this