Browsing SUNY Polytechnic Institute by Subject "canny edge detection"
Now showing items 1-1 of 1
Detection of Brain Tumor in Magnetic Resonance Imaging (MRI) Images using Fuzzy C-Means and ThresholdingAlthough many clinical experts or radiologists are well trained to identify tumors and other abnormalities in the brain, the identification, detection and segmentation of the affected area in the brain is observed to be a tedious and time consuming task. MRI has been a conventional and resultant image processing technique to visualize structures of the human body. It is very difficult to visualize abnormal structures of the brain using simple imaging techniques. MRI technique uses many imaging modalities that scan and capture the internal structure of the human brain. Even with the use of these techniques, it is a difficult and tedious task for a human eye to be always sophisticated in detecting brain tumors from these images. With emerging technology, we can provide a way to ease the process of detection. This project focuses on identification of brain tumor in MR images, it involves in removing noise using noise removal technique AMF followed by enhancing the images using Balance Enhancement Contrast technique (BCET).Further, image segmentation is performed using fuzzy c-means and finally the segmented images are produced as an input to a canny edge detection resulting with the tumor image. This report entices the approach, design, and implementation of the application and finally the results. I have tried implementing/developing this application in Python. The Jupyter notebook provides a block simulation for the entire flow of the project.