The DICOM CT Image Compression Based On Enhanced Lossless Prediction And Multilevel Thresholding Based Hybrid Cuckoo Search With Hill Climbing (CS-HC) Algorithm Based Segmentation

In computer vision applications, image segmentation is a common image processing step. It is used to separate pixels into different groups. The rise in the threshold count would hinder the segmentation phase of images. At the same time, in the field of threshold implementation in the image, it becomes an NT concern. This thesis suggests a multilevel threshold based on optimization techniques to remove ROI and uses enhanced lossless prediction algorithm to compress DICOM images in telemedicine applications. The hybrid Cuckoo search with hill climbing (CS-HC) algorithm strengthens the process used by the search agent to update the optimal solution. This algorithm calculates the threshold value. The superior results are produced by the proposed multilevel level thresholding based on CS-HC, as seen by the simulation results. Optimization is efficient and it has a high degree of convergence. Effective results are provided by the proposed lossless compression algorithm based on classification and blending estimation as compared with JPEG lossless and lossy compression techniques. With various threshold values, the algorithm 's efficiency is checked. To apply this algorithm, Matlab2010a is used and DICOM photos are used to validate it.