A novel 2D wavelet level energy for breast lesion classification on ultrasound images

Yueh Ching Liao, King Chu Hung, Shu Mei Guo, Po Chin Wang, Tsung Lung Yang

Research output: Chapter in Book/Report/Conference proceedingChapter


Infiltrative nature is a unique characteristic of breast cancer. Cross-sectional view of infiltrative nature can find a rough lesion contour on ultrasound image. Roughness description is crucial for clinical diagnosis of breast lesions. Based on boundary tracking, traditional roughness descriptors usually suffer from information loss due to dimension reduction. In this paper, a novel 2-D wavelet-based energy feature is proposed for breast lesion classification on ultrasound images. This approach characterizes the roughness of breast lesion contour with normalized spatial frequency components. Feature efficacies are evaluated by using two breast sonogram datasets with lesion contour delineated by an experienced physician and the ImageJ, respectively. Experimental results show that the new feature can obtain excellent performance and robust contour variation resistance.

Original languageEnglish
Title of host publicationThe Era of Interactive Media
PublisherSpringer New York
Number of pages10
ISBN (Electronic)9781461435013
ISBN (Print)1461435005, 9781461435006
Publication statusPublished - 2013 Oct 1

All Science Journal Classification (ASJC) codes

  • General Computer Science


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