An Improved Image Denoising and Segmentation Approach for Detecting Tumor from 2-D MRI Brain Images

Image denoising and segmentation are the two most challenging fields in medical image processing particularly when it is application specific. The presence of noise not only degrades the visual quality but also immensely affects the accuracies of segmentation which is essential for medical diagnosis process. In this paper, we present an improved approach for denoising and segmentation of 2-D magnetic resonance brain images for detecting the tumor. We use fourth order partial differential equation based technique for removing MRI noises and thereby applied segmentation using automatic seeded region growing algorithm for detecting brain tumor automatically. The contribution of this work is the use of compass operator to preserve the anatomically significant information at the edges and a new morphological technique for skull removal from the brain MRI image which leads to the process of detecting tumor accurately. The method is tested on several real brain MRI images and it shows 100% success in detecting tumor automatically.