Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Picture Processing
MAGNETIC RESONANCE IMAGING (MRI) SIMULATION ON A GRID COMPUTING ARCHITECTURE
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A review of vessel extraction techniques and algorithms
ACM Computing Surveys (CSUR)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A Comparison of Seven Techniques for Choosing Subsets of Pattern Recognition Properties
IEEE Transactions on Computers
MaZda-A software package for image texture analysis
Computer Methods and Programs in Biomedicine
Clustering stability-based feature selection for unsupervised texture classification
Machine Graphics & Vision International Journal
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Personalized identification of abdominal wall hernia meshes on computed tomography
Computer Methods and Programs in Biomedicine
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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.