Segmentation of Calcification and Brain Hemorrhage with Midline Detection

A considerable amount of deformities are prone to be neglected by the radiotherapists besides acquisition of excessive and false positive rates. In this article, a process has been developed representing the selective parameters with Fuzzy C-Means and ANFIS (Adaptive Neuro-fuzzy inference). As it is a grave concern to differentiate the hemorrhage and calcification on the basis of selective factors. The desirable outcome and useful information are most likely to be delivered by the fuzzy clustering segmentation methods. The abnormal tissues in the brain are accurately identified by our proposed method. ANFIS can be referred to as the extension of the ANN family and it displays excellent learning skills and estimating competences, so it is believed to be a highly productive tool through which ambiguities in any system can be efficiently managed. The medical discipline is realizing the benefits of this system, such as the identification of hemorrhage and calcification. Our method is beneficial to image fusion techniques whose applications rely on the source information of local images. Results show that our method is superior then the other traditional methods in terms of quantitative image segmentation performance parameters.