title: Automatic selection of parameters for vessel/neurite segmentation algorithms.
abstract: An automated method is presented for selecting optimal parameter settings for vessel/neurite segmentation algorithms using the minimum description length principle and a recursive random search algorithm. It trades off a probabilistic measure of image-content coverage against its conciseness. It enables nonexpert users to select parameter settings objectively, without knowledge of underlying algorithms, broadening the applicability of the segmentation algorithm, and delivering higher morphometric accuracy. It enables adaptation of parameters across batches of images. It simplifies the user interface to just one optional parameter and reduces the cost of technical support. Finally, the method is modular, extensible, and amenable to parallel computation. The method is applied to 223 images of human retinas and cultured neurons, from four different sources, using a single segmentation algorithm with eight parameters. Improvements in segmentation quality compared to default settings using 1000 iterations ranged from 4.7%-21%. Paired t-tests showed that improvements are statistically significant (p < 0.0005). Most of the improvement occurred in the first 44 iterations. Improvements in description lengths and agreement with the ground truth were strongly correlated (p = 0.78).
authors.affiliation: Rensselaer Polytechnic Institute, Troy, NY 12180, USA. abdulm@ecse.rpi.edu
authors.forename: Muhammad-Amri
authors.initials: MA
authors.lastname: Abdul-Karim
authors.forename: Badrinath
authors.initials: B
authors.lastname: Roysam
authors.forename: Natalie M
authors.initials: NM
authors.lastname: Dowell-Mesfin
authors.forename: Andreas
authors.initials: A
authors.lastname: Jeromin
authors.forename: Murat
authors.initials: M
authors.lastname: Yuksel
authors.forename: Shivkumar
authors.initials: S
authors.lastname: Kalyanaraman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002478:Cells, Cultured; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D008833:Microcirculation / Q000166:cytology*; D008853:Microscopy / Q000379:methods*; D016501:Neurites / Q000648:ultrastructure*; D009474:Neurons / Q000000981:diagnostic imaging; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography