Brain tumor segmentation and its area calculation in brain MR images using K-mean clustering and Fuzzy C-mean

Abstract:

          Brain tumor identification is really challenging task in early stages of life. But now it became advanced with deep-learning. Now a day’s issue of brain tumor automatic identification is of great interest. In Order to detect the brain tumor of a patient we consider the data of patients like MRI images of a patient’s brain. Here our problem is to identify whether tumor is present in patients brain or not. It is very important to detect the tumors at starting level for a healthy life of a patient. Finally deep neural networks classifier is applied then result image will compared with the dataset images and it will display whether it is benign or malignant.

Proposed System:

            We are providing new methods of MRI images of Brain of a patient. The images are pre processed and further segmented for the required feature. Then feature Extraction is done for the images by GLCM features. Region of interest (ROI) segmentations is applied in order to identify the affected portion of tumor. Here the threshold required for segmenting adjusts itself according to the segmented area and position. Finally classification applied through a deep neural networks then result image will compared with the dataset images and it will display whether it is benign or malignant.