title: A Hybrid Approach for Improving Image Segmentation: Application to Phenotyping of Wheat Leaves.
abstract: In this article we propose a novel tool that takes an initial segmented image and returns a more accurate segmentation that accurately captures sharp features such as leaf tips, twists and axils. Our algorithm utilizes basic a-priori information about the shape of plant leaves and local image orientations to fit active contour models to important plant features that have been missed during the initial segmentation. We compare the performance of our approach with three state-of-the-art segmentation techniques, using three error metrics. The results show that leaf tips are detected with roughly one half of the original error, segmentation accuracy is almost always improved and more than half of the leaf breakages are corrected.
authors.affiliation: Phenomics and Bioinformatics Research Centre, University of South Australia, Mawson Lakes, South Australia, Australia.
authors.forename: Joshua
authors.initials: J
authors.lastname: Chopin
authors.affiliation: School of Engineering and Information Technology, Murdoch University, Perth, Western Australia.
authors.forename: Hamid
authors.initials: H
authors.lastname: Laga
authors.affiliation: Phenomics and Bioinformatics Research Centre, University of South Australia, Mawson Lakes, South Australia, Australia.
authors.forename: Stanley J
authors.initials: SJ
authors.lastname: Miklavcic
mesh_terms: D007091:Image Processing, Computer-Assisted / Q000379:methods*; D018515:Plant Leaves / Q000033:anatomy & histology*; D014908:Triticum / Q000033:anatomy & histology*