A cascade-learning approach for automated segmentation of tumour epithelium in colorectal cancer

Computer Science

• Learning Approach 100%
• Image Analysis 100%
• Experimental Result 50%
• Training Sample 50%
• Set Formulation 50%
• Intensity Inhomogeneity 50%
• Histological Image 50%
• fuzzy c mean 50%
• Segmentation Method 50%
• Shape Information 50%
• Illumination Variation 50%
• Classification Framework 50%
• Tumor Segmentation 50%

Engineering

• Image Analysis 100%
• Learning Approach 100%
• Colorectal 100%
• Experimental Result 50%
• Limited Number 50%
• Segmentation Method 50%
• Illumination Variation 50%
• Set Formulation 50%
• Intensity Inhomogeneity 50%
• Level Set 50%
• Region of Interest 50%
• Additive Noise 50%