A novel and robust automatic seed point selection method for breast ultrasound images

Rashid Al Mukaddim, Juan Shan, Irteza Enan Kabir, Abdullah Salmon Ashik, Rasheed Abid, Zhennan Yan, Dimitris N. Metaxas, Brian S. Garra, Kazi Khairul Islam, S. Kaisar Alam

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

Abstract

Accurate segmentation of breast lesions is among the several challenges in the development of a fully automatic Computer-Aided Diagnosis system for solid breast mass classification. Many high level segmentation methods rely heavily on proper initialization and the seed point selection is usually the necessary first step. In this paper, a fully automatic and robust seed point selection method is proposed. The method involves a number of processing steps in both space and frequency domain and endeavors to incorporate the breast anatomical knowledge. Using a database of 498 images, we compared the proposed method with two other state-of-the-art methods; the proposed method outperforms both methods significantly with a success rate of 62.85% vs. 44.97% and 13.05% on seed point select.

Original languageEnglish (US)
Title of host publication1st International Conference on Medical Engineering, Health Informatics and Technology, MediTec 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509054213
DOIs
StatePublished - Jan 26 2017
Event1st International Conference on Medical Engineering, Health Informatics and Technology, MediTec 2016 - Dhaka, Bangladesh
Duration: Dec 17 2016Dec 18 2016

Publication series

Name1st International Conference on Medical Engineering, Health Informatics and Technology, MediTec 2016

Other

Other1st International Conference on Medical Engineering, Health Informatics and Technology, MediTec 2016
Country/TerritoryBangladesh
CityDhaka
Period12/17/1612/18/16

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Computer Science Applications
  • Health Informatics
  • Health(social science)

Keywords

  • Breast-cancer
  • Computer-aided diagnosis (CAD)
  • Image-segmentation
  • Quantitative-ultrasound
  • Ranking-function
  • Seed-point
  • Sonography
  • Tissue-Characterization
  • ultrasound

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