A Review on Brain Tumour Segmentation

Abstract: The data mining is a research field of intersects with many other field such as Artificial Intelligence, Statistics, Visualization, Parallel Computing and Image Processing. Data mining techniques are Brain MRI image classification that can diagnose brain tumor and other diseases. This paper present the current research being carried out using the data mining techniques for the diagnosis of brain tumor. The following algorithms have been identified: Decision Trees, Support Vector Machine, Artificial Neural Networks and Fuzzy C-Means, K-means cluster. The analysis it is very difficult to name a single data mining algorithm as the most suitable for the brain tumor detection or classification. The segmentation of brain tumor is measured to be one of the complicated procedures in medical field. The MRI brain tumors segmentation is a composite process as the location of the edema region is much to identify. The intensity of the tumors differs in every patient which makes the exact boundary location of the lesions to appear blurred in the MRI images. This paper present also provides a critical evaluation of the literature reviewed, which reveals new facets of brain tumor segmentation.

Keywords: Data Mining, Brain Tumor Segmentation.