Neural network analysis applied to tumor segmentation on 3D breast ultrasound images

Sheng Fang Huang, Yen Ching Chen, Kyung Moon Woo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

Our study presents a fully automatic tumor segmentation method using three-dimensional (3D) breast ultrasound (US) images. The proposed method is an approach based on 2D image processing techniques, which considers the variations of contours between two adjacent planes in a 3D dataset. In this approach, a reference image obtained in the previous plane was used to facilitate the segmentation in the next plane. To determine the initial reference image, we extracted five features from regions in each 2D slice and applied neural network analysis to discriminate the tumor from the background. Finally, three area error metrics were calculated to measure the overall performance of the system.

Original languageEnglish
Title of host publication2008 5th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, Proceedings, ISBI
Pages1303-1306
Number of pages4
DOIs
Publication statusPublished - 2008 Sep 10
Event2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI - Paris, France
Duration: 2008 May 142008 May 17

Publication series

Name2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI

Conference

Conference2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
CountryFrance
CityParis
Period08-05-1408-05-17

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

Fingerprint Dive into the research topics of 'Neural network analysis applied to tumor segmentation on 3D breast ultrasound images'. Together they form a unique fingerprint.

Cite this