Geometric transform invariant Brain-MR image analysis for tumor detection

In this work we propose a translational, rotational and scaling invariant scheme for possible detection of tumors in Brain-Magnetic Resonance (MR) images. The method incorporates the features like shape, position and texture to accurately diagnose from the infected images. The geometric transformation invariant nature of the method helps in detecting the tumor in various scales, positions and orientations, at a better rate compared to the state-of-the art methods. The method combines three features (shape, position and texture) to form a feature vector, which is used for detecting the infected parts in the image. In order to improve the accuracy of detection process, we employ a preprocessing step to denoise and enhance the images. The result section details the analysis and results of the proposed method and highlights on the accuracy of the method to properly identify the tumor parts in an MR image.