Medical imaging creates use of the technology to non-invasively make known the interior structure of the human body. By way of medical imaging modalities patient’s life be able to be improved throughout an accurate and rapid remedial without any side effects. The most important reason of this paper is to increased methodology that may accurately classify a tumor growth from abnormal tissues. This examination work has distinguished region growing segmentation Technique and implement and reflect. From our validation test comes about, this examination work uncovered that this model neglects to create useful groups order force effect poor tumor characterization precision. The images used for tumor segmentation are obtained from MRI modality. During this research, we’ve developed an image segmentation technique based on catchments basins and ridge lines to segment the brain image accurately. This technique relies on interest techniques for extracting tumor portion of the images in the watershed segmentation. Direct application of watershed leads to over-segmentation because of the presence of noise and different irregularities accepted in digital images. Thus to avoid this, we’ve carried out some preprocessing to eliminate noise present within the MRI images through acquisition stage. Then mask the watershed segmentation. After preprocessing step, we have a tendency to calculate the morphological operation of the input images. This exists one of the real issues to predict the tumor design and to address this issue; the masked watershed Segmentation Technique is proposed and resolute overall Sensitivity, Specificity, and Accuracy.

MRI Brain tumor morphological operation Watershed segmentation and Masked-Watershed Segmentation.