Medical Image Segmentation Techniques
This cluster of papers covers advances in image segmentation techniques, particularly focusing on medical image analysis, graph cuts, active contours, MRI segmentation, deformable image registration, level set methods, statistical shape models, deep learning, and texture analysis.
Papers listed on taxonomy pages are the top few works per node from the OpenAlex snapshot. That list is not exhaustive and is not an endorsement. The topic map and the journal registry remain separate: there is still no authoritative topic-to-venue or topic-to-organization edge. Search is a lexical lookup, not a claim that a venue publishes a topic.
Most cited
- A Computational Approach to Edge Detection
- Normalized cuts and image segmentation
- Scale-space and edge detection using anisotropic diffusion
- AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages
- Mean shift: a robust approach toward feature space analysis
- Fast robust automated brain extraction
Most recent
- Non-integral geometry: Additional term fA as a regularizing term
- Contrastive Discrepancy: A label-free metric for deformable image registration supporting testing-time hyperparameter selection.
- Image Denoising and Segmentation Using Piecewise Smooth Relaxed Total Generalized Variation
- Enhanced arithmetic optimization algorithm for K-means-based breast MRI segmentation
- Geometry in Variational Models for Image Segmentation
- Monotonic System Framework for Globally Optimal Structuring of Complex Data