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Grading of breast carcinoma plays an important role for identification of severity of the disease and prognostic treatment in histopathology. It depends on three indices: tubule formation, nuclear plemorphism, and mitotic count by scoring based on degrees of differentiation. However, scoring on these indices often subject to intra- and inter-observer variability due to its complexities and manual classifications by pathologists.
This study aims to accurately detect lumen and gland of duct for scoring of tubule formation and nuclear plemorphism using a series of image processing techniques so that robust grade identification can be conducted in the future. To demonstrate the performance of the proposed method, H&E stained images acquired from biopsy tissues of patients were tested. Experimental results reveal that the proposed algorithms can obtain satisfactory segmentations compared to manual segmentation by an experienced pathologist. These results can provide reliable scores in the grade of breast carcinoma.
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