International Journal of Engineering and Technology

This paper presents an accurate detection of a brain tumor by initial prediction of the tumor stage with the help of tumor infected magnetic resonance (MR) images. The accuracy of any brain tumor segmentation scheme depends on its ability to separately identify different classes of tissues. In fact, segmentation can be considered as the most important process in evaluating the characterization, delineation, and visualization of extracted portion i.e. region of interest from the MR images. This paper evaluates the ability of discrete cosine transformation and lifting wavelet transformation efficiently to segment different tissue classes and detect the tumor infected area through the MR images; thus, providing an improved technique which can help radiologists to accurately identify brain tumor grades, its exact location, size and its current stage. To the best of our knowledge and through the analysis, this is the first study of its kind that utilizes the performance of a discrete cosine transformation (DCT) enabled lifting wavelet transformation based clustering technique in detecting brain tumor. To summarize, our DCT enabled lifting wavelet transformation (LWT) based brain tumor detection algorithm abbreviated as DELWT (DCT Enabled Lifting Wavelet Transformation) provides promising efforts in brain tumor classification, detection, and extraction. It also has the potential for analysis and guiding methodology that are applicable in automatic analysis of larger data sets of MR images. The simulation results prove the significance and efficacy of the proposed mechanism in comparison to the existing techniques.