Quantitative microscopic evaluation of mucin areas and its percentage in mucinous carcinoma of the breast using tissue histological images

• This algorithm provides mucin index (MI), and achieved 97.74% segmentation accuracy.
• MI can differentiate MC into pure, moderately, and minimally mucinous.
• This algorithm can differentiate MC into various grades (I, II, and III).
• The proposed algorithm is much more efficient than FCM, for mucin segmentation.

Mucinous carcinoma (MC) of the breast is very rare (∼1–7% of all breast cancers), invasive ductal carcinoma. Presence of pools of extracellular mucin is one of the most important histological features for MC. This paper aims at developing a quantitative computer-aided methodology for automated identification of mucin areas and its percentage using tissue histological images. The proposed method includes pre-processing (i.e., colour space transformation and colour normalization), mucin regions segmentation, post-processing, and performance evaluation. The proposed algorithm achieved 97.74% segmentation accuracy in comparison to ground truths. In addition, the percentage of mucin present in the tissue regions is calculated by the mucin index (MI) for grading MC (pure, moderately, minimally mucinous).

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