Zwiggelaar, R.
* 1996: Finding Orientated Line Patterns in Digital Mammographic Images
* 1997: Detecting Stellate Lesions in Mammograms via Statistical Models
* 1999: Separating Background Texture and Image Structure in Mammograms
* 2000: Novel Similarity Measure to Evaluate Image Correspondence, A
* 2001: Tracking Mammographic Structures Over Time
* 2002: Automated Quality Assurance Applied to Mammographic Imaging
* 2002: Image Processing in Scale-Orientation Space
* 2002: Synthesis and Analysis of Solid Texture: Application in Medical Imaging
* 2004: Linear Structures in Mammographic Images: Detection and Classification
* 2004: Texture based segmentation: Automatic Selection of Co-occurrence Matrices
* 2005: Automatic Classification of Breast Density
* 2005: Automatic Classification of Breast Tissue
* 2005: Improving ASM Search Using Mixture Models for Grey-Level Profiles
* 2006: Semantic Analysis on Medical Images: A Case Study
* 2007: Risk Classification of Mammograms Using Anatomical Linear Structure and Density Information
* 2010: Image classification: A novel texture signature approach
* 2010: Iterative Hyperplane Merging: A Framework for Manifold Learning
* 2011: Automatic Estimation of the Number of Segmentation Groups Based on MI
* 2011: Joint Histogram Modelling for Segmentation Multiple Sclerosis Lesions
* 2011: Tracking 3D human pose with large root node uncertainty
* 2012: Guest Editorial: Scenes, Images and Objects
* 2012: Region-based active surface modelling and alpha matting for unsupervised tumour segmentation in PET
* 2012: Topographic representation based breast density segmentation for mammographic risk assessment
* 2013: Automated Mammographic Risk Classification Based on Breast Density Estimation
* 2013: Breast Parenchymal Pattern Analysis in Digital Mammography: Associations between Tabár and Birads Tissue Compositions
* 2013: database for facial behavioural analysis, A
* 2013: Local Feature Based Breast Tissue Appearance Modelling for Mammographic Risk Assessment
* 2013: Manifold Learning for Density Segmentation in High Risk Mammograms
* 2013: Segmentation for Multiple Sclerosis Lesions Based on 3D Volume Enhancement and 3D Alpha Matting
* 2013: Unsupervised Cell Nuclei Segmentation Based on Morphology and Adaptive Active Contour Modelling
* 2014: Improved BET Method for Brain Segmentation, An
* 2014: Modelling mammographic microcalcification clusters using persistent mereotopology
* 2016: Classification of mammographic microcalcification clusters using a combination of topological and location modelling
* 2016: Fixing the root node: Efficient tracking and detection of 3D human pose through local solutions
* 2018: Confidence Analysis for Breast Mass Image Classification
* 2018: Mammographic mass classification using filter response patches
* 2024: Feature learning based on connectivity estimation for unbiased mammography mass classification
Includes: Zwiggelaar, R. Zwiggelaar, R.[Reyer]
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