Fuzzy Rule based Classification of Microscopic Images of Squamous Cell Carcinoma of Esophagus

Abstract

In this paper, we present a novel method based on active contours for segmentation and fuzzy rule based classification of microscopic images of esophagus tissues obtained from the abnormal regions of human esophagus detected through endoscopy. This method is used for classification of Squamous Cell Carcinoma (SCC) of esophagus, namely, well differentiated (WD), moderately differentiated (MD), and poorly differentiated (PD) SCC. The multi-grid active contour method is used for cell nuclei segmentation, three geometric features are used feature extraction and a fuzzy rule based classifier is built for classification of SCC. The experimental results demonstrate the efficacy of the proposed method.

Performance of a simple chromatin-rich segmentation algorithm in quantifying basal cell carcinoma from histology images

“…One possibility would be to use the active contours method in order to evolve a curve representing the boundaries from the ROI [ 31 ]. Recently, this method has been explored in order to segment histology images [ 32 - 34 ]. One potential drawback when using active contours is that some implementations require the user to manually specify an initial boundary.…”

Textural pattern classification for oral squamous cell carcinoma

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Automated identification of keratinization and keratin pearl area from in situ oral histological images

“…Squamous cell carcinoma in oesophagus has been identified using texture information from the histological section of oesophagus (Hiremath et al, 2007). Later they proposed fuzzy rule based approach for classifying squamous cell carcinoma of oesophagus (Hiremath and Iranna, 2011). Nuclear architecture for different cancer condition (especially for oropharyngeal p16+) has been modelled based on spatially aware cell cluster graphs approach (Ali et al, 2013).…”