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Detection of Goblet Cells to diagnose Barrett’s Esophagus using Contourlet Transform and GLCM approach

The computer-based cell and study of tissue plays a vital role in medical informatics, however, there is a increase in recognition since the advancement in hardware. A ten years ago this process started, and from that time, it has been a vital research field in healthcare informatics.The ultimate aim of this work is to design and implement a new technique, which can minimize the time taken for detecting prognostic factors of oesophageal cancer and helping the pathologists and specialists. The principal purpose of this software is to detect Barrett’s oesophagus as it has a firm association with esophageal adenocarcinoma. The digital slides from virtual microscopes helps in the detection. This proposed method will reduce the processing time and improves accuracy in detecting goblet cell. The algorithms used in this system are Contourlet Transform-Decomposition, and Gray-Level Co-Occurance Matrix (GLCM) - Feature Extraction. A matrix describing the frequency of one gray level appearing in a specified spatial linear relationship with another gray level within the stipulated area of investigation is GLCM. Therefore, the co occurrence matrix is executed based on two components that is, the relative distance between the pixel pair and its relative orientation