Integrating edge detection and fuzzy connectedness for automated segmentation of anatomical branching structures

  • Authors:

  • Angeliki Skoura;Tatyana Nuzhnaya;Vasileios Megalooikonomou

  • Affiliations:

  • Computer Engineering and Informatics Department, University of Patras, Patras University Campus, 26504, Greece;Center for Data Analytics and Biomedical Informatics, Temple University, Temple University Campus, 19122 Philadelphia, USA;Computer Engineering and Informatics Department, University of Patras, Patras University Campus, 26504, Greece/ Center for Data Analytics and Biomedical Informatics, Temple University, Temple Univ ...

  • Venue:
  • International Journal of Bioinformatics Research and Applications
  • Year:
  • 2014

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Abstract

Image segmentation algorithms are critical components of medical image analysis systems. This paper presents a novel and fully automated methodology for segmenting anatomical branching structures in medical images. It is a hybrid approach which integrates the Canny edge detection to obtain a preliminary boundary of the structure and the fuzzy connectedness algorithm to handle efficiently the discontinuities of the returned edge map. To ensure efficient localisation of weak branches, the fuzzy connectedness framework is applied in a sliding window mode and using a voting scheme the optimal connection point is estimated. Finally, the image regions are labelled as tissue or background using a locally adaptive thresholding technique. The proposed methodology is applied and evaluated in segmenting ductal trees visualised in clinical X-ray galactograms and vasculature visualised in angiograms. The experimental results demonstrate the effectiveness of the proposed approach achieving high scores of detection rate and accuracy among state-of-the-art segmentation techniques.