Medical Image Segmentation Using Firefly Algorithm and Enhanced Bee Colony Optimization

-MRI and Mammogram is one of the best technologies currently being used for diagnosing breast cancer and brain tumour. Breast cancer and brain tumour is diagnosed at advanced stages with the help of the mammogram and MRI image. In this thesis an intelligent system is designed to diagnose tumour through mammograms, using image processing techniques along with intelligent optimization tools, such as Fire Fly Algorithm (FFA), Enhanced BEE Colony Optimization (EBCO) and Artificial Neural Network. The detection of tumour is performed in two phases: preprocessing and segmentation in the first phase and feature extraction, selection and classification in the second phase. 350 MRI images obtained from KMCH Hospital Coimbatore and 161 pairs of digitized mammograms obtained from the Mammography Image Analysis Society (MIAS) database is used to design the proposed diagnosing system. Initially, the film artifacts and X-ray labels are removed from the images and median filter is applied to remove the high frequency components from the image. The suspicious region is segmented using Markov Random Field (MRF) hybrid with EBCO and FFA algorithm for MRI and mammogram images. The MRF and EBCO and FFA algorithm based image segmentation method is a process seeking the optimal labeling of the pixels. The optimum label is that which minimizes the Maximizing a Posterior (MAP) estimate. EBCO and FFA metaheuristic algorithm is implemented to compute the optimum label, which is to be treated as an optimum threshold for segmentation. Keywords--MRI, Mammogram, Enhancement, Feature Extraction, Receiver Operating Characteristics