Optimized Liver Tumor Detection and Segmentation using Neural Network

Abstract

Image processing has become an essential component in many fields of biomedical research such as tumor detection, automatically determining the volume of a heart chamber, screening lung scans for possible diseases. Different techniques for automatic detection of liver tumor involve various steps: image acquisition, segmentation, classification using neural network and optimization, and identification of tumor type. Most common segmentation approaches are: Region based, Threshold based, Level set, Clustering based and Edge detection. Liver tumor segmentation is done with region based approach in this research work. Region based methods partition an image into regions that are similar according to a set of predefined criteria where as other segmentation approaches like edge detection methods partition an image according to rapid changes in intensity near the edges. In this research work Particle Swarm Optimization (PSO) and Seeker Optimization algorithm (SOA) have been compared for classification of tumor using CT scan images. The main focus of this work is to detect liver tumor and compare results of PSO and SOA in term of detection and classification accuracy and elapsed time. Region based segmentation approach has been used for segmentation of liver and liver tumor from CT scan images. PSO and SOA are used for classification and PSO optimization gives better results in term of detection and classification accuracy and elapsed time. For liver tumor classification, PSO results with as 93.3% detection and classification accuracy where as SOA results in 60% detection and classification accuracy.