Brain Tumor Segmentation Using K-Means and Fuzzy C-Means Clustering and Its Area Calculation and Stage Using SVM Algorithm

Abstract

This work denote the implementation of various techniques for detection of range and shape of tumor in brain MR images and identifies stage of tumor from the given area of tumor. Existing work in developed countries prove that the numbers of people who have brain tumors were died due to the fact of inaccurate detection. After researching a lot statistical analysis which is based on those people whose are affected in brain tumor some general Risk factors and Symptoms have been discovered. The development of technology in science day night tries to develop new methods of treatment. This image is visually defined by the physician for detection & diagnosis of brain tumor. However this method accurate determines the accurate of stage & size of tumor and identifies stage of tumor from the area of tumor. This work uses segmentation of brain tumor based on the k-means clustering and fuzzy c-means clustering.This technique helps for the segmentation of tumor with accuracy compare to manual segmentation. It also reduces the time for analysis and finds the stage of tumor from the given area of tumor.

Brain Tumour Segmentation in FLAIR MRI Using Sliding Window Texture Feature Extraction Followed by Fuzzy C-Means Clustering

“…The core downside of this method is that it uses several monotonous steps for the segmentation. In (Pingale & Todmal, 2018), the authors have modelled Fuzzy C-Means (FCM) and k-means on the T1 contrast axial plane MRI scans for the extraction of the tumorous region through the histogram steered initialization of the cluster. K-means algorithm is more proficient than FCM although FCM recognizes scarcely classes of three tissues.…”