Accurate and fully automatic segmentation of breast ultrasound images by combining image boundary and region information

Mohammad I. Daoud, Ayman A. Atallah, Falah Awwad, Mahasen Al-Najar

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

11 Citations (Scopus)

Abstract

Breast ultrasound image segmentation is challenging task due to the low quality of ultrasound images and the complex breast structure. An accurate and automatic algorithm is presented to segment breast ultrasound images by combining image boundary and region information. The algorithm decomposes the image into a set of superpixels using the Normalized Cuts method along with texture analysis. An SVM classifier is employed to estimate the tumor likelihood of each superpixel based on five texture features. A seed superpixel is identified based on the tumor likelihoods and spatial locations of the superpixels. The seed superpixel is extended to accurately highlight the tumor region using a region growing approach that combines both the superpixels tumor likelihoods and edge-based analysis. The proposed algorithm and two popular segmentation algorithms are used to segment 50 breast ultrasound images. The proposed algorithm achieved higher sensitivity and lower error rates compared to the two existing algorithms.

Original languageEnglish
Title of host publication2016 IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2016 - Proceedings
PublisherIEEE Computer Society
Pages718-721
Number of pages4
ISBN (Electronic)9781479923502
DOIs
Publication statusPublished - Jun 15 2016
Event2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Prague, Czech Republic
Duration: Apr 13 2016Apr 16 2016

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2016-June
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
Country/TerritoryCzech Republic
CityPrague
Period4/13/164/16/16

Keywords

  • Cancer detection
  • Computer-aided diagnosis
  • Image segmentation
  • Normalized Cuts
  • Region growing
  • Superpixels
  • Texture analysis
  • Ultrasound imaging

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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