Detection of Brain Tumor and Extraction of Features in MRI Images Using K-means Clustering and Morphological Operations

In the past couple of decades, progressive research has been done in the medical imaging field to accurately detect brain tumors. This is vital for early treatment and saves time. This report presents a method of automatic detection of brain tumor in magnetic resonance imaging (MRI) images. The proposed algorithm consists of three stages: image acquisition, pre-processing and post-processing. In image acquisition, a database of brain tumor MRI images is developed with the help of publicly available online sources. The pre-processing stage involves converting the image to grayscale and applying noise filters to enhance the image. In the post-processing stage, k-means clustering algorithm is used to separate the image into four clusters of varying intensity levels. The cluster with the tumor is found and then the tumor is extracted by using morphological and region properties operations. Features like energy, contrast, kurtosis, correlation and homogeneity are found along with the area and perimeter of the tumor and the accuracy of the extraction. These extracted features can be used in the future to more accurately classify brain tumors with the help of neural networks. The proposed method has given good results and accuracy.