Abstract
For many years, brain tumor detection has been one of the most essential and competitive issues for medical researchers. Many methods have been developed to detect normal and abnormal tissues in Magnetic Resonance (MR) images. In this work, we present a novel algorithm based on iterative Co-Clustering and K-Means (ICCK). After image pre-processing and enhancement, this algorithm recognizes the part of the image that contains the tumor and eliminates the unused parts using a modification of the Co-Clustering method. Finally, the K-Means clustering method is adopted to detect the tumor area. The Co-Clustering methods cannot be used directly for the detection of brain tumors because they manipulate the image matrix for the purpose of block clustering. Furthermore, they are incapable of detecting the tumor area correctly and accurately. Such issues are addressed by our proposed methodology. The latent block model (LBM) is applied as the Co-Clustering method in this work. We evaluate the performance of our method on the images that were collected from the BraTS2019 dataset. The sensitivity, specificity, accuracy, and dice similarity coefficient values for our method are 82.41%, 99.74%, 99.28%, and 84.87%, respectively, which shows that the proposed method outperforms the existing methods in the literature. Moreover, it performs much better on complex images.