Accurate segmentation of touching cells in multi-channel microscopy images with geodesic distance based clustering

Keyphrases

• Accurate Segmentation 100%
• Microscopic Image 100%
• Geodesic Distance 100%
• Distance-based Clustering 100%
• Touching Cells 100%
• Multi-channel Microscopy 100%
• Individual Cells 75%
• Cell number 25%
• Ras Proteins 25%
• Madin-Darby Canine Kidney (MDCK) Cells 25%
• Morphological Changes 25%
• Selection Strategy 25%
• Drug Discovery 25%
• Nuclei Segmentation 25%
• Segmentation Method 25%
• Low Signal-to-noise Ratio 25%
• Biomedical Studies 25%
• Kidney Epithelial Cells 25%
• Target Discovery 25%
• Protein Expression Pattern 25%
• Learning Scheme 25%
• Clustering Approach 25%
• N-Ras 25%
• Segmentation Accuracy 25%
• Adaptive Learning 25%
• Cell Segmentation 25%
• Cluster Center 25%
• Cell Boundary 25%
• Dual-mode Imaging 25%
• Seed Selection 25%
• Intensity Contrast 25%

Computer Science

• Individual Cell 100%
• Geodesic Distance 100%
• Noise-to-Signal Ratio 33%
• Experimental Result 33%
• Segmentation Method 33%
• Pattern Expression 33%
• clustering approach 33%
• Quantitative Comparison 33%
• Biomedical Study 33%
• Learning Scheme 33%
• Adaptive Learning 33%
• segmentation accuracy 33%
• Cell Segmentation 33%
• Multichannel Image 33%

Engineering

• Multichannel 100%
• Individual Cell 75%
• Signal-to-Noise Ratio 25%
• Experimental Result 25%
• Segmentation Method 25%
• Expression Pattern 25%
• Boundary Cell 25%
• Multichannel Image 25%
• Selection Procedure 25%
• Learning Scheme 25%