title: Multi-focus image fusion using epifluorescence microscopy for robust vascular segmentation.
abstract: Automatic segmentation of three-dimensional mi-crovascular structures is needed for quantifying morphological changes to blood vessels during development, disease and treatment processes. Single focus two-dimensional epifluorescent imagery lead to unsatisfactory segmentations due to multiple out of focus vessel regions that have blurred edge structures and lack of detail. Additional segmentation challenges include varying contrast levels due to diffusivity of the lectin stain, leakage out of vessels and fine morphological vessel structure. We propose an approach for vessel segmentation that combines multi-focus image fusion with robust adaptive filtering. The robust adaptive filtering scheme handles noise without destroying small structures, while multi-focus image fusion considerably improves segmentation quality by deblurring out-of-focus regions through incorporating 3D structure information from multiple focus steps. Experiments using epifluorescence images of mice dura mater show an average of 30.4% improvement compared to single focus microvasculature segmentation.
authors.forename: Rengarajan
authors.initials: R
authors.lastname: Pelapur
authors.forename: V B Surya
authors.initials: VB
authors.lastname: Prasath
authors.forename: Filiz
authors.initials: F
authors.lastname: Bunyak
authors.forename: Olga V
authors.initials: OV
authors.lastname: Glinskii
authors.forename: Vladislav V
authors.initials: VV
authors.lastname: Glinsky
authors.forename: Virginia H
authors.initials: VH
authors.lastname: Huxley
authors.forename: Kannappan
authors.initials: K
authors.lastname: Palaniappan
mesh_terms: D000465:Algorithms; D000818:Animals; D001808:Blood Vessels / Q000033:anatomy & histology*; D007091:Image Processing, Computer-Assisted; D051379:Mice; D008853:Microscopy / Q000379:methods*