Nuclear Segmentation and its Quantification in H&E Stained Images of Oral Precancer to Detect its Malignant Potentiality

TLDR: This algorithm uses differential contrast enhancement and distance map transformations to segment out the cell nuclei in ImageJ Software and performed successfully on high magnification images with high speed and relative simplicity thus proving its credibility.

Abstract: Diagnosis of oral cancer using pathology is becoming more dependent on digital imaging. Since precancerous conditions like Oral submucous fibrosis originate in the basal layer of the tissue, it is very important to investigate the cell nuclei of the basal layer in Haematoxylin and Eosin stained tissue as it contains diagnostically important information. For that, accurate identification and segmentation of the nuclei is imperative. Our algorithm uses differential contrast enhancement and distance map transformations to segment out the cell nuclei in ImageJ Software. The algorithm performed successfully on high magnification images with high speed and relative simplicity thus proving its credibility. The nuclear attributes like entropy, polarity, and compactness are calculated and the values obtained are then statistically analyzed using Mann-Whitney U Test using SPSS Software to differentiate between normal and OSF(with severe dysplasia and without dysplasia). The results showed that in case of entropy, statistical significant difference $(\mathbf{p} is present between all the above mentioned three classes but in cases of compactness and polarity, statistical significant differences are present between normal and diseased classes, but not between OSF (without dysplasia) and OSF (with severe dysplasia) cases for both attributes $(\mathbf{p}=\pmb{0.1527}$ for compactness and $\mathbf{p}\pmb{=0.6965}$ for polarity).