title: Multiquadric Spline-Based Interactive Segmentation of Vascular Networks.
abstract: 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.
authors.affiliation: Computational Imaging and VisAnalysis Lab, Department of Computer Science, Columbia, MO 65201 USA.
authors.forename: Sachin
authors.initials: S
authors.lastname: Meena
authors.affiliation: Computational Imaging and VisAnalysis Lab, Department of Computer Science, Columbia, MO 65201 USA.
authors.forename: V B
authors.initials: VB
authors.lastname: Surya Prasath
authors.affiliation: Computational Imaging and VisAnalysis Lab, Department of Computer Science, Columbia, MO 65201 USA.
authors.forename: Yasmin M
authors.initials: YM
authors.lastname: Kassim
authors.affiliation: Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand; Harvard TH Chan School of Public Health, Harvard University, Boston, USA.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Maude
authors.affiliation: Research Service, Harry S. Truman Memorial Veterans Hospital, Columbia, MO 65201 USA; Department of Medical Pharmacology and Physiology, University of Missouri-Columbia, MO 65211 USA.
authors.forename: Olga V
authors.initials: OV
authors.lastname: Glinskii
authors.affiliation: Research Service, Harry S. Truman Memorial Veterans Hospital, Columbia, MO 65201 USA; Department of Pathology and Anatomical Sciences, University of Missouri-Columbia, MO 65211 USA.
authors.forename: Vladislav V
authors.initials: VV
authors.lastname: Glinsky
authors.affiliation: Department of Medical Pharmacology and Physiology, University of Missouri-Columbia, MO 65211 USA; National Center for Gender Physiology, University of Missouri-Columbia, MO 65211 USA.
authors.forename: Virginia H
authors.initials: VH
authors.lastname: Huxley
authors.affiliation: Computational Imaging and VisAnalysis Lab, Department of Computer Science, Columbia, MO 65201 USA.
authors.forename: Kannappan
authors.initials: K
authors.lastname: Palaniappan
mesh_terms: D000465:Algorithms; D000818:Animals; D004388:Dura Mater / Q000098:blood supply*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D055806:Microvessels / Q000033:anatomy & histology* / Q000000981:diagnostic imaging; D061848:Optical Imaging / Q000379:methods