COMPARATIVE ANALYSIS OF VARIOUS MEDICAL IMAGE SEGMENTATION METHODOLOGIES IN TEMPORAL ORDER
Pamela Juneja, Sharanjit Kaur, Harsh Vardhan Sharma, Pawan Kumar · Journal of Natural Remedies · 2020
Medical image processing is a vast and emerging field. By this medical practitioners are able to inquest into structure, functions and pathology of a human body. Basically image processing process comprised of following basic stages: enhancement, segmentation, quantification, registration, visualisation and also involves compression, storage and communication. Medical image processing made it possible to diagnose various dangerous diseases like cancer and tumour at early stage. But extracting correct boundaries of infected region through segmentation is a major challenges. There are many techniques to do the segmentation of medical images like energy based methods, Active contour methods, region based, clustering based, Fuzzy Logic based, Hybrid methods, Markov Random field based models, ANN, deformable models. Also classification of image segmentation methods can be based on generations like first generation implemented region growing algorithms, second generation algorithms implemented uncertainty models, pattern recognitions and clustering based algorithms and third generation implemented Atlas based segmentation and rule based segmentation methods. Researchers basically aim to get a accurate and automatic segmentation technique with up to date software. This papers provides valuable insights into trends of methodologies followed in image segmentation fields along with the major software used at industry level as well as academic level.