Automated brain tumor detection of MRI image based on hybrid image processing techniques

Primary challenges are the identification, segmentation, and extraction of afflicted area from scanning magnetic resonance. However, it is a time-consuming tiresome for clinical specialists. In this paper, an automated brain tumor system proposed. The proposed employs hybrid image processing techniques such as contrast correction, histogram normalization, thresholding techniques, arithmetic, morphological operations to quarantine nearby organs other tissue improving localization affected region. At first, skull stripping process segregate non-designated regions extract designated regions. Those resultant region images further subjected discover tumor. planned scheme studied on resonance (MR) with use T1, T2, T1c, fluid-attenuated inversion recovery (FLAIR). method employed. results reveal that quite efficient accuracy rate segmentation separation interest in reached 95%. Finally, significance procedure confirmed using real dataset got ten patients were diagnosed begin, malignant, metastatic tumors Al-Yarmouk Baghdad teaching hospital Baghdad, Iraq.