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

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.

Improved Fuzzy Connectedness Segmentation Method for Medical Images with Multiple Seeds in MRI

“…Bejar and Miranda combined the concept of fuzzy connectedness with the direction of region growing, excluding the results which were illogical [17]. Skoura et al combined the fuzzy connectedness with feature detection of target area [18]. And Rueda et al combined the fuzzy connectedness with the feature detection of the target region [19].…”