#69: Level-set-based histology image segmentation with region-based comparison


Abstract

Automated histological grading of tissue biopsies for clinical cancer care is a challenging problem that requires sophisticated algorithms for image segmentation, tissue archi- tecture characterization, global texture feature extraction, and high-dimensional clustering and classification algorithms. Cur- rently there are no automatic image-based grading systems for establishing the pathology of cancer tissues. A primary step in computational histology is accurate image segmentation to detect significant regions such as nuclei, lumen and epithelial cytoplasm which together make up a gland structure. We describe a new approach for tissue segmentation using fuzzy spatial clustering and level set active contours. The proposed technique shows improvement in segmentation accuracy and outperform the classical clustering and level set methods, when compared to ground truth segmentation.