Human Epithelial Cell Image Analysis and Segmentation using Threshold Based Fusion Technique

Abstract

The most demanding aspect of digital image processing is segmenting an image efficiently. Cell segmentation or classifying cells in an image is essential while analyzing cell images in medical research, especially in spot diagnosis, cancer cell detection, and live-cell imaging segmentation forms a crucial component. This research examines existing segmentation algorithms and suggests a new segmentation technique that employs image filtering and thresholding. Thresholding is an essential part of image analysis and segmentation. Finally, the segmented image and the FCM (fuzzy C-means) based clustered image are merged. In terms of accuracy, sensitivity, dice-coefficient, and Jaccard-coefficient, the simulation coupled with ground truth data is proven to produce better segmentation outcomes.