Incorporating domain knowledge into the fuzzy connectedness framework: Application to brain lesion volume estimation in multiple sclerosis

Mark A. Horsfield, Rohit Bakshi, Marco Rovaris, Mara A. Rocca, Venkata S R Dandamudi, Paola Valsasina, Elda Judica, Fulvio Lucchini, Charles R G Guttmann, Maria Pia Sormani, Massimo Filippi

Research output: Contribution to journalArticlepeer-review

Abstract

A method for incorporating prior knowledge into the fuzzy connectedness image segmentation framework is presented. This prior knowledge is in the form of probabilistic feature distribution and feature size maps, in a standard anatomical space, and "intensity hints" selected by the user that allow for a skewed distribution of the feature intensity characteristics. The fuzzy affinity between pixels is modified to encapsulate this domain knowledge. The method was tested by using it to segment brain lesions in patients with multiple sclerosis, and the results compared to an established method for lesion outlining based on edge detection and contour following. With the fuzzy connections (FC) method, the user is required to identify each lesion with a mouse click, to provide a set of seed pixels. The algorithm then grows the features from, the seeds to define the lesions as a set of objects with fuzzy connectedness above a pre-set threshold. The FC method gave improved inter-observer reproducibility of lesion volumes, and the set of pixels determined to be lesion was more consistent compared to the contouring method. The operator interaction time required to evaluate one subject was reduced from an average of 111 minutes with contouring to 16 minutes with the FC method.

Original languageEnglish
Pages (from-to)1670-1680
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume26
Issue number12
DOIs
Publication statusPublished - Dec 2007

Keywords

  • Fuzzy connectedness
  • Lesion volume
  • MRI
  • Multiple sclerosis
  • Prior knowledge

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology
  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computational Theory and Mathematics

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