Fast segmentation of ultrasound images using robust Rayleigh distribution decomposition

Chi Young Ahn, Yoon Mo Jung, Oh In Kwon, Jin Keun Seo

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

The segmentation of left ventricle in ultrasound imaging of human heart would provide an important clinical parameter for the evaluation of cardiac functions including volume stroke or ejection fraction and wall motion tracking. We propose a fast segmentation method to reduce laborious manual efforts and conveniently provide robust and stable cardiac quantification to users. The proposed method provides a very simple energy functional form using a predetermined Rayleigh distribution parameter so that the corresponding steepest descent approach with some shape constraints on contour is still capable of fast segmentation. We present several experimental results on two-dimensional echocardiography data for the performance of the proposed model. The experiments show that the proposed model is especially useful when a part of target boundary is seriously corrupted.

Original languageEnglish
Pages (from-to)3490-3500
Number of pages11
JournalPattern Recognition
Volume45
Issue number9
DOIs
Publication statusPublished - 2012 Sep 1

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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