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Commonly used drawing tools for interactive image segmentation and labeling include active contours or boundaries, scribbles, rectangles and other shapes. Thin vessel shapes in images of vascular networks are difficult to segment using automatic or interactive methods. This paper introduces the novel use of a sparse set of user-defined seed points (supervised labels) for precisely, quickly and robustly segmenting complex biomedical images. A multiquadric spline-based binary classifier is proposed as a unique approach for interactive segmentation using as features color values and the location of seed points. Epifluorescence imagery of the dura mater microvasculature are difficult to segment for quantitative applications due to challenging tissue preparation, imaging conditions, and thin, faint structures. Experimental results based on twenty epifluorescence images is used to illustrate the benefits of using a set of seed points to obtain fast and accurate interactive segmentation compared to four interactive and automatic segmentation approaches.

Original publication

DOI

10.1109/EMBC.2016.7592074

Type

Conference paper

Publication Date

08/2016

Volume

2016

Pages

5913 - 5916

Addresses

Computational Imaging and VisAnalysis Lab, Department of Computer Science, Columbia, MO 65201 USA.

Keywords

Dura Mater, Animals, Mice, Algorithms, Image Processing, Computer-Assisted, Microvessels, Optical Imaging