Robust and Accurate Automated Methods for Detection and Segmentation of Brain Tumor in MRI

Abstract

In this proposed study, a novel Multimodal brain MR image segmentation method is presented to overcome the unattractive and undesirable over segmentation characteristics of conventional Watershed method. The proposed work, presents Optimal Region Amalgamation Technique (RAT) that merge the Watershed method (spatial domain) and Fuzzy C-means clustering (feature spaces) to reduce the unattractive and undesirable over segmentation in brain MR images. In the proposed work, to improve the quality of segmentation results of Watershed method, initially it construct a RAG(Region Merging Graph) for optimal RAT by applying the most popular MRF(Markov Random Field) method . Consequently, the inter-region comparison is presented by applying the watershed method in Spatial Domain and Fuzzy C-Means clustering method in Feature Space for image mapping to compute the Optimal Region Amalgamation. Further, to determine the Feature space and domain space illustration of the brain MR image segmentation, the SGD (Spatial Graph Depiction) is presented that is computed with FSD (Feature Space Depiction) which is obtained by watershed partitioning and FCM clustering method. The experimental results on multimodal brain MR image datasets presents that the proposed novel Optimal Region Amalgamation Technique (RAT) exhibits more promising MR images segmentation results with compared to the traditional watershed method. Finally, an assessment and evaluation of the state-of-the-art brain tumor segmentation methods are presented and future directions to improve and standardize the detection and segmentation of brain tumor into daily clinical treatment are addressed.