Interactive Brain Edema Tumor Segmentation In Different Modality Images With A Combined Novel Neural Network

Abstract

MR/CT images are facing the difficulties during investigation of tumours in brain part especially in edema portions. Different modalities has lot of information to measure and find out the exact boundary range. MRI and CT images are playing a vital role in medical field to find out and curing the tumour in any area. If the image has big in size and has high amount of information of the MRI AND CT images, then it takes the time to register, filter and segment that images.  This research article represents a programmed brain tumour detection technique to expand the exactness and yield and reduction the finding time. An essential role in the investigation of tumours is played by precise identification of the location and size of the brain tumour. The three stages of the analytic strategy are pre-handling of the MRI images, extraction, and grouping. The highlights are segregated based on Dual-Tree Complex wavelet modification following image histogram balance (DTCWT). At the very end, Back Propagation Neural Networks (BPN) is employed to explain the normal and dysfunctional brains. The Spatial Fuzzy K-Means Clustering effectively estimates tumour detection.