Development of pancreatic CT – scan image dataset and retrieval process for diagnosis

Abstract

This study presents medical CT scan image feature analysis, creation of data bank and development of a data mining technique. A dataset of 50 known pancreas digital CT scan images with their clinical diagnosis were composed. All images were subjected for image textual characters (energy, entropy, contrast, homogeneity and correlation), which were statistically calculated in numerical MAT lab environment with syntax. Gray level co-occurrence matrix was utilized for interpretation of 2D digital images. Data was compared and mean values were maintained for retracing a matched image with query image. Results were discussed with future innovation and scope in medical digital image diagnosis.