Iterative sliced inverse regression for segmentation of ultrasound and MR images

DOI10.1016/j.patcog.2007.04.019zbMath1122.68720OpenAlexW2003435520MaRDI QIDQ996432

Han-Ming Wu, Henry Horng-Shing Lu

Publication date: 14 September 2007

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2007.04.019

zbMATH Keywords

dimension reductionsupport vector machinesmultidimensional scalingsliced inverse regressionK-meansnearest mean classifierunsupervised clustering

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies for image processing (68U10) Pattern recognition, speech recognition (68T10) Computing methodologies and applications (68U99)

Related Items (4)

Entropy-based sliced inverse regression ⋮ Probability density difference-based active contour for ultrasound image segmentation ⋮ Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images ⋮ Isometric sliced inverse regression for nonlinear manifold learning

Uses Software

• LIBSVM
• ElemStatLearn
• SVMTorch
• AS 136

Cites Work

• Algorithm AS 136: A K-Means Clustering Algorithm
• Support-vector networks
• Multidimensional scaling. I: Theory and method
• 10.1162/15324430152733142
• Determining the Dimension in Sliced Inverse Regression and Related Methods
• Sliced Inverse Regression for Dimension Reduction
• On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
• Theory & Methods: Special Invited Paper: Dimension Reduction and Visualization in Discriminant Analysis (with discussion)
• The elements of statistical learning. Data mining, inference, and prediction
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