2010/01/11 by Burcu Aydin, Burcu Aydın, Gábor Pataki +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Artificial intelligence #Cell Image Analysis Techniques #Computer science #Context (archaeology) #Covariate #Data Visualization and Analytics #Data mining #Data set #Data structure #Machine learning #Mathematics #Medical Image Segmentation Techniques #Pattern recognition (psychology) #Representation (politics) #Set (abstract data type) #Tree (set theory) #Tree structure #stat.AP
paper · pdf · doi:10.1214/11-ejs612
published as Electronic Journal of Statistics 2011, Vol. 5, 405-420 · 17 pages, 8 figures
arxiv created 2010/01/11 · openalex publication_date 2011/01/01 · arxiv updated 2012/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
This study introduces a new method of visualizing complex tree structured objects. The usefulness of this method is illustrated in the context of detecting unexpected features in a data set of very large trees. The major contribution is a novel two-dimensional graphical representation of each tree, with a covariate coded by color. The motivating data set contains three dimensional representations of brain artery systems of 105 subjects. Due to inaccuracies inherent in the medical imaging techniques, issues with the reconstruction algorithms and inconsistencies introduced by manual adjustment, various discrepancies are present in the data. The proposed representation enables quick visual detection of the most common discrepancies. For our driving example, this tool led to the modification of 10% of the artery trees and deletion of 6.7%. The benefits of our cleaning method are demonstrated through a statistical hypothesis test on the effects of aging on vessel structure. The data cleaning resulted in improved significance levels. Our second example analyses brain artery images of healthy patients and patients with brain tumor. Our visualization can identify tumor patients.