Graph cut method for automatic tumor detection in prediction based reconstructed MR image

Among the different types of tumor in human body, brain tumor is one of the most life-threatening diseases and it should be detected accurately in early stage for proper treatment. Recently magnetic resonance imaging (MRI) is found to be efficient in detecting tumor as it does not need any opening on the human body. Presence of tumor in brain may be detected by segmenting the captured MR images. Tumor segmentation, however, is performed manually by radiologists which is a time consuming and also a subjective process. Furthermore, presence of the radiologists in a distant location often needs transmission over a radio mobile channel for which a high bandwidth is required. Keeping these in mind, in this work, an efficient low bandwidth transmission of the captured MRI brain images followed by its reconstruction and graph cut based automatic segmentation of the tumor is proposed. Compressed sensing (CS) is used to offer the facility of low bandwidth transmission. Extensive simulation results are also shown for reconstructed images followed by segmentation results.