Optimum fuzzy filters for phase-contrast magnetic resonance imaging segmentation

Kartik S. Sundareswaran, David H. Frakes, Mark A. Fogel, Dennis D. Soerensen, John N. Oshinski, Ajit P. Yoganathan

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


Purpose: To develop and validate a multidimensional segmentation and filtering methodology for accurate blood flow velocity field reconstruction from phase-contrast magnetic resonance imaging (PC MRI). Materials and Methods: The proposed technique consists of two steps: (1) the boundary of the vessel is automatically segmented using the active contour approach; and (2) the noise embedded within the segmented vector field is selectively removed using a novel fuzzy adaptive vector median filtering (FAVMF) technique. This two-step segmentation process was tested and validated on 111 synthetically generated PC MRI slices and on 10 patients with congenital heart disease. Results: The active contour technique was effective for segmenting blood vessels having a sensitivity and specificity of 93.1% and 92.1% using manual segmentation as a reference standard. FAVMF was the superior technique in filtering out noise vectors, when compared with other commonly used filters in PC MRI (P < 0.05). The peak wall shear rate calculated from the PC MRI data (248 ± 39 sec -1), was significantly decreased to (146 ± 26 sec -1) after the filtering process. Conclusion: The proposed two-step segmentation and filtering methodology is more accurate compared to a single-step segmentation process for post-processing of PC MRI data.

Original languageEnglish (US)
Pages (from-to)155-165
Number of pages11
JournalJournal of Magnetic Resonance Imaging
Issue number1
StatePublished - Jan 2009


  • Active contours
  • Fuzzy systems
  • Noise filtering
  • PC MRI
  • Segmentation
  • Vector median filtering

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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