Near-set Based Mucin Segmentation in Histopathology Images for Detecting Mucinous Carcinoma

AbstractThis paper introducesnear-set based segmentation method for extraction and quantification of mucin regions for detecting mucinouscarcinoma (MC which is a sub type of Invasive ductal carcinoma (IDC)). From histology point of view, the presence of mucin is one of the indicators for detection of this carcinoma. In order to detect MC, the proposed method majorly includes pre-processing by colour correction, colour transformation followed by near-set based segmentation and post-processing for delineating only mucin regions from the histological images at 40 ×. The segmentation step works in two phases such asLearn andRun.In pre-processing step, white balance method is used for colour correction of microscopic images (RGB format). These images are transformed into HSI (Hue, Saturation, and Intensity) colour space and H-plane is extracted in order to get better visual separation of the different histological regions (background, mucin and tissue regions). Thereafter, histogram in H-plane is optimally partitioned to find set representation for each of the regions. InLearn phase, features of typical mucin pixel and unlabeled pixels are learnt in terms of coverage of observed sets in the sample space surrounding the pixel under consideration. On the other hand, inRun phase the unlabeled pixels are clustered as mucin and non-mucin based on itsindiscernibilty with ideal mucin, i.e. their feature values differ within a tolerance limit. This experiment is performed for grade-I and...
Source: Journal of Medical Systems - Category: Information Technology Source Type: research