Main Article Content
Background: One of the most significant missions in medical imaging analysis is imaging segmentation and is the most important part in a lot of clinical applications. For analysis of the brain MRI and its changes, describing pathological areas, and for surgical processing, the segmentation is often applied for visualizing and measuring the anatomical parts of the brains. The aim of this study is to do manual segmentation because automatic brain tumor segmentation has various challenges. Methods: A 3 Tesla MRI scanner with gadolinium contrast medium was used for imaging the patient’s brain and discovering the tumor. Moreover, manual segmentation (3D slicer program) was applied on the tumor directly to get the best identification and special information. Finally Measure the size of the tumor in MRI through MRIcro program for allows efficient viewing and exporting of the brain image. Results: The proposed Dicom (DCM) model was considerable to segment the brain with valid accuracy. Manual image segmentation showed the best identification of the special information of tumor and displayed separate healthy tissue from tumor region to give accurate results for diagnosis and treatment planning. Conclusion: The proposed method (Manual image segmentation) is good for segmenting the brain in 3D MR images with perfect accuracy. Furthermore, it requires the radiologist to use the multi-modality information presented by the MRI images along with anatomical and physiological knowledge gained through training and experience and segmentation results are subject to large intra and inter rater variability.