Segmentation of brain tissues is one important process prior to many analysis and visualization tasks for Magnetic Resonance (MR) images. Edge is one of the important characteristic features used in many image segmentation techniques for brain tissue segmentation in MR image. Richer Convolutional Features approximation is technique used for detection of edges in any image. Unfortunately, MR images always contain significant amount of noise caused by operator performance, equipment and the environment. This noise can lead to major inaccuracies in edge detection process and hence in segmentation result. We conduct the research in measuring the performance of Richer Convolutional Features Edge Detector approximation for edge detection in different noise level for single-channel MR image. To validate the accuracy and robustness of Richer Convolutional Features Edge Detector approximation we carried out experiments on simulated MR brain scans. The performance of edge detector is analyzed by different quantitative measures. These quantitative measures include the mathematical measures like mean square error, signal to noise ratio and peak signal to noise ratio as well the statistical measures like accuracy, sensitivity, specificity and F measure

Brain tissue classification, Edge detection, F measure Magnetic Resonance, MR Images, Richer Convolutional Features approximation, Segmentation, Sensitivity, Specificity