Abstract:
Bio-medical image segmentation is one of the most important studies in the field of
medicine, as well as the results obtained by the diagnostic guide for diagnosis, treatment
planning and verifi cation of managed treatments. Therefore, accuracy is as important in
analysis of bio-medical images as we have accuracy in data acquisition systems.
Medical image segmentation helps in the research of brain tumor prediction, biopsy,
guidance, prognosis monitoring, disease stage identi fication, therapy planning, and therapy response. The Tumor is the accumulation of abnormal cells. [1] Brain tumor is the most common cancer in infants and adolescent. In the case of Glioma, It is the most common form of the malignant tumor. which are heterogeneous in nature. starting the diagnosis earlier will help them to extend their valuable life. Nowadays fully automatic methods have been able to achieve state-of- art results using computed tomography images NS Magnetic Resonance Image which could give better tissue images. Bio-medical imaging system requires the consecutive
application of different kinds of imaging techniques to be used to quantify and analyze
the predicted characteristics. The main purpose of this dissertation is to model algorithm
that enables the analysis and quanti cation of various functions in medical imaging with
minimal input dependence on results. As part of this dissertation study, a comprehensive literature review is conducted and a new groundwork for analysis and processing of medical images is implemented, including factors subjected to the automation of individual processes
