title: 3D image texture analysis of simulated and real-world vascular trees.
abstract: A method is proposed for quantitative description of blood-vessel trees, which can be used for tree classification and/or physical parameters indirect monitoring. The method is based on texture analysis of 3D images of the trees. Several types of trees were defined, with distinct tree parameters (number of terminal branches, blood viscosity, input and output flow). A number of trees were computer-simulated for each type. 3D image was computed for each tree and its texture features were calculated. Best discriminating features were found and applied to 1-NN nearest neighbor classifier. It was demonstrated that (i) tree images can be correctly classified for realistic signal-to-noise ratio, (ii) some texture features are monotonously related to tree parameters, (iii) 2D texture analysis is not sufficient to represent the trees in the discussed sense. Moreover, applicability of texture model to quantitative description of vascularity images was also supported by unsupervised exploratory analysis. Eventually, the experimental confirmation was done, with the use of confocal microscopy images of rat brain vasculature. Several classes of brain tissue were clearly distinguished based on 3D texture numerical parameters, including control and different kinds of tumours - treated with NG2 proteoglycan to promote angiogenesis-dependent growth of the abnormal tissue. The method, applied to magnetic resonance imaging e.g. real neovasculature or retinal images can be used to support noninvasive medical diagnosis of vascular system diseases.
authors.affiliation: Institute of Electronics, Technical University of Lodz, Lodz, Poland. marek.kocinski@p.lodz.pl
authors.forename: Marek
authors.initials: M
authors.lastname: Kociński
authors.forename: Artur
authors.initials: A
authors.lastname: Klepaczko
authors.forename: Andrzej
authors.initials: A
authors.lastname: Materka
authors.forename: Martha
authors.initials: M
authors.lastname: Chekenya
authors.forename: Arvid
authors.initials: A
authors.lastname: Lundervold
mesh_terms: D000465:Algorithms; D000818:Animals; D001932:Brain Neoplasms / Q000473:pathology*; D002533:Cerebral Angiography / Q000379:methods*; D003198:Computer Simulation; D005910:Glioma / Q000473:pathology*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018613:Microscopy, Confocal / Q000379:methods*; D008954:Models, Biological; D009389:Neovascularization, Pathologic / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D051381:Rats; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity