Quantitative analysis of breast echotexture patterns in automated breast ultrasound images

Ruey Feng Chang, Yu Ling Hou, Chung Ming Lo, Chiun Sheng Huang, Jeon Hor Chen, Won Hwa Kim, Jung Min Chang, Min Sun Bae, Woo Kyung Moon

研究成果: 雜誌貢獻文章同行評審

7 引文 斯高帕斯(Scopus)


Purpose: Breast tissue composition is considered to be associated with breast cancer risk. This study aimed to develop a computer-aided classification (CAC) system to automatically classify echotexture patterns as heterogeneous or homogeneous using automated breast ultrasound (ABUS) images. Methods: A CAC system was proposed that can recognize breast echotexture patterns in ABUS images. For each case, the echotexture pattern was assessed by two expert radiologists and classified as heterogeneous or homogeneous. After neutrosophic image transformation and fuzzy c-mean clusterings, the lower and upper boundaries of the fibroglandular tissues were defined. Then, the number of hypoechoic regions and histogram features were extracted from the fibroglandular tissues, and the support vector machine model with the leave-one-out cross-validation method was utilized as the classifier. The authors' database included a total of 208 ABUS images of the breasts of 104 females. Results: The accuracies of the proposed system for the classification of heterogeneous and homogeneous echotexture patterns were 93.48% (43/46) and 92.59% (150/162), respectively, with an overall Az (area under the receiver operating characteristic curve) of 0.9786. The agreement between the radiologists and the proposed system was almost perfect, with a kappa value of 0.814. Conclusions: The use of ABUS and the proposed method can provide quantitative information on the echotexture patterns of the breast and can be used to evaluate whether breast echotexture patterns are associated with breast cancer risk in the future.
頁(從 - 到)4566-4578
期刊Medical Physics
出版狀態已發佈 - 8月 1 2015

ASJC Scopus subject areas

  • 生物物理學
  • 放射學、核子醫學和影像學


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