A Novel Approach to Improve Sobel Edge Detector

An improved edge detection algorithm based on k-means clustering approach. Being a fundamental tool in image processing, edge detection aims to identify the points in an image at which image brightness changes sharply or regularly. In Medical Science, edge detection is very useful, such as in segmentation of MRI image. Magnetic resonance imaging (MRI) produces a detailed image of any human body part, by using the natural magnetic properties of the body tissues. Since body tissues contain hydrogen atoms, which made to emit radio signals. These radio signals are then detected by a scanner. Magnetic Resonance imaging is a medical test used to diagnose tumors of the brain on the basis of high quality images produced by it. In this paper edge detection is made to determine the location of a tumor. The edge detection technique presented in this paper uses k-means clustering approach to generate the initial groups. These groups are then input to the mamdani fuzzy inference system, which generates different threshold parameters. When these parameters are fed into the classical sobel edge detector, it is found that images obtained are more enhanced and provide exact location of a tumor in a brain.